Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: Using Computer Modeling for Tracking high-order Nonlinear Systems with Time-Delay
Abstract: This paper investigates the challenge of globally practically tracking a class of high-order nonlinear systems with time delay via feedback. The object of study is a nonlinear system, the main goal is to develop a control function that allows you to regulate the output data of the specified nonlinear system using a reference signal. The article publication is distinguished by its scientific novelty due to the study of a system in which the degree of the upper limit of nonlinearity is determined within a continuous interval. The system under examination exhibits a distinctive characteristic, wherein the extent of the upper boundary limitation of nonlinearity is defined within a continuous interval. We propose a customizable and delay-independent tracking controller designed to mitigate the impact of delay time non-linearity. This is achieved by generating a Lyapunov function using a generalized approach that involves incorporating a signum function and a power integrator. The resulting controller ensures the global boundedness of all states within the closed system, along with a sufficiently small control error over a finite time. Additionally, this work places emphasis on computer simulations to validate previously obtained results.
Keywords: Nonlinear systems;tracking;signum function;time delay;computational modeling;Matlab graphical user interface (GUI)
Review score: Moderate
Review comment: The article discusses the challenge of tracking a class of high-order nonlinear systems with a time delay using computer modeling. It proposes a tracking controller to mitigate the impact of delay and nonlinearity, utilizing a Lyapunov function with a signum function and power integrator. The controller ensures global boundedness and minor control errors over time. The study emphasizes computer simulations for validation. 
Despite the significant work that the authors have done, in order to accept the article for publication, it is necessary to correct some comments:
1. The article must be formatted in accordance with the requirements of the conference
2. The scientific novelty of the publication, the subject and object of the research, and the objectives must be clearly stated in the abstract.
3. Lack of clarity in the presentation of mathematical equations and concepts
4. There is limited discussion of the proposed tracking controller's practical implementation and real-world applicability for high-order nonlinear systems with time delay.
5. The article can provide more detailed explanations or examples to help readers better understand the significance and implications of the research findings.

After correcting these comments, the article can be accepted for participation in the conference
Title: Statistical analysis of outcomes of basivertebral nerve radiofrequency ablation for vertebrogenic chronic low back pain management
Abstract: In the era of office work, many people suffer from chronic low back pain (CLBP). Intra-osseous basivertebral nerve radiofrequency ablation (BVN RFA) is considered a way to treat vertebrogenic CLBP. However, the BVN RFA was not well studied. Using PubMed, Scopus, Google Scholar, and the Cochrane Library, we found four studies of BVN RFA from the US and performed a statistical analysis of its effect on CLBP. We compared the sample statistics of the studies on pain intensity and LBP-related disability both in pre- and postoperative stages in two groups: treatment and control. Using the RevMan v. 5.4 software, we calculated the mean difference (MD) for each outcome between the groups and constructed forest plots. On a 0-10 pain scale, our results showed the postoperative pain intensity reduction in the treatment group compared to the control group (MD with 95% CI is –1.63 [–2.91, –0.35], p-value = 0.01). There was a considerable improvement in disability index as well (MD with 95% CI is –10.36 [–22.87, 2.14] on a 0-50 scale, p-value = 0.10), but the result was not statistically significant. Our analysis concludes that the intra-osseous BVN RFA is effective in reducing vertebrogenic CLBP, but more randomized controlled trials would be beneficial to study the effect of BVN RFA in more detail.
Keywords: radiofrequency ablation;disability;pain intensity score;meta-analysis;medical decision making
Review score: Moderate
Review comment: The abstract provides a concise overview of the study; however, it could be strengthened by including more specific details about the statistical methods used and the main findings of the meta-analysis.
While the paper mentions previous studies on BVN RFA, providing a more comprehensive review of the existing literature, including recent systematic reviews and meta-analyses, would strengthen the background section.
The methods section provides a clear description of the study protocol and statistical analysis. However, more details about the inclusion and exclusion criteria for selecting studies, as well as the search strategy used to identify relevant literature, would enhance the transparency and reproducibility of the study.
The results section presents the findings of the meta-analysis in a clear and organized manner. However, it would be beneficial to include forest plots for both pain intensity and disability outcomes to visually represent the effect sizes and confidence intervals of each study. Ensure that all references cited in the paper are accurately formatted and relevant to the topic under investigation. Consider including recent studies and systematic reviews/meta-analyses to support the rationale for the study and its findings.
Also, please correct the format of the figures and tables.
Title: Statistical analysis of outcomes of basivertebral nerve radiofrequency ablation for vertebrogenic chronic low back pain management
Abstract: In the era of office work, many people suffer from chronic low back pain (CLBP). Intra-osseous basivertebral nerve radiofrequency ablation (BVN RFA) is considered a way to treat vertebrogenic CLBP. However, the BVN RFA was not well studied. Using PubMed, Scopus, Google Scholar, and the Cochrane Library, we found four studies of BVN RFA from the US and performed a statistical analysis of its effect on CLBP. We compared the sample statistics of the studies on pain intensity and LBP-related disability both in pre- and postoperative stages in two groups: treatment and control. Using the RevMan v. 5.4 software, we calculated the mean difference (MD) for each outcome between the groups and constructed forest plots. On a 0-10 pain scale, our results showed the postoperative pain intensity reduction in the treatment group compared to the control group (MD with 95% CI is –1.63 [–2.91, –0.35], p-value = 0.01). There was a considerable improvement in disability index as well (MD with 95% CI is –10.36 [–22.87, 2.14] on a 0-50 scale, p-value = 0.10), but the result was not statistically significant. Our analysis concludes that the intra-osseous BVN RFA is effective in reducing vertebrogenic CLBP, but more randomized controlled trials would be beneficial to study the effect of BVN RFA in more detail.
Keywords: radiofrequency ablation;disability;pain intensity score;meta-analysis;medical decision making
Review score: Moderate
Review comment: 1. It’s recommended to improve methods parts. 
2. Also it’s required to describe your developed model. Give some mathematical justification of the developed model. Else, it’s subject to not consider your article because it does not correspond to the topics of the conference.
Title: Control of nonlinear system by means of feedback using the Python-control library
Abstract: This document offers an overview of fundamental functionalities within the Python Control Systems Library (python-control), a software tool in Python tailored for designing control systems. Emphasizing the creation of a state-space control system for a kinematic vehicle, it covers aspects such as trajectory creation and implementing gain feedback control. It illustrates the use of the input/output (I/O) system class, which can be used to build models of nonlinear control systems.The article substantiates the necessity of controlling a nonlinear system by means of feedback using the Python-control library. It is shown that one of the most effective means of its development is the Python programming language. Due to the presence of many libraries, it has a sufficient set of tools for its realization. Built-in functions of mathematical packages NumPy and SciPy, intended for controlling a nonlinear system by means of feedback, are considered. The possibilities of application of the Matplotlib package for computer modeling related to the system with feedback are shown, which is performed when the vehicle moves along a given trajectory using a linear controller. and the built-in Tkinter package for the development of a graphical interface of the resource under development.
Keywords: feedback control;high-order nonlinear systems
Review score: Low
Review comment: Dear authors,

I would like to thank you for submitting your work.

We would have liked to see a more detailed description of the key findings of the study in the abstract. State what specific conclusions have been drawn from your research and what practical or theoretical implications they have. I also suggest paying attention to the clarity and brevity of the wording in the abstract so that it is easier for readers to grasp the essence and importance of your research.
In addition, all formulas (2.1-3.1) are unreadable on the left side, and some have extra symbols in vector forms, which makes it difficult to understand the logic and analysis based on these formulas. It is recommended that all formulas be carefully reviewed and adjusted to ensure clarity and consistency to avoid confusion and erroneous conclusions.
Title: Control of nonlinear system by means of feedback using the Python-control library
Abstract: This document offers an overview of fundamental functionalities within the Python Control Systems Library (python-control), a software tool in Python tailored for designing control systems. Emphasizing the creation of a state-space control system for a kinematic vehicle, it covers aspects such as trajectory creation and implementing gain feedback control. It illustrates the use of the input/output (I/O) system class, which can be used to build models of nonlinear control systems.The article substantiates the necessity of controlling a nonlinear system by means of feedback using the Python-control library. It is shown that one of the most effective means of its development is the Python programming language. Due to the presence of many libraries, it has a sufficient set of tools for its realization. Built-in functions of mathematical packages NumPy and SciPy, intended for controlling a nonlinear system by means of feedback, are considered. The possibilities of application of the Matplotlib package for computer modeling related to the system with feedback are shown, which is performed when the vehicle moves along a given trajectory using a linear controller. and the built-in Tkinter package for the development of a graphical interface of the resource under development.
Keywords: feedback control;high-order nonlinear systems
Review score: Low
Review comment: The scientific novelty is not shown in clear way. It's proposed to improve the abstract. The text of the article should be revisioned. The figures numeration does not match their references in the text. One formula misses symbols.
Title: The Effects of Augmented Reality to Enhance the Didactic Methodology in Schooling Physics
Abstract: The prompt progression of AR tools currently releases innovative chances intended for schools globally to expand the excellence of schooling and erudition capability applying AR. The objective of this research is to progress AR app to improve the didactic tactic in education. AR app apply a symbol - based technique for conducting computer-generated items hooked on actual real-life. This caused in the arrangement of study content and expertise that could by applied in schooling. Throughout the challenging study experiment, pupils' attention is awakened and it level marks it extra keen to study the physics. This is an outstanding involvement to the didactic area concluded AR and educating the pedagogic tactic in schooling. The scientific novelty of the article is evaluate mobile augmented reality applications against headset complete AR environment. The research utilized performance assessment instruments, questionnaires, and interviews with students and lecturers to gather primary data. The study involved 70 high school students in Kazakhstan during the 2023–2024 academic year. The findings indicated that the use of AR-based learning media had a positive impact on students' comprehension of basic concepts and skills. The validation results from material experts showed a high validity rate of 91.37%. The study suggests that the use of mobile AR can enhance the interactive and engaging aspects of learning, thereby increasing students' interest and understanding.
Keywords: AR;teaching;mobile application;physics learning
Title: Enhancing Gameplay Experience Through Reinforcement Learning in Games
Abstract: It is important to improve the game development field since games have great influence on economics and society. One of the ways to enhance game experience is to replace non-playable characters (NPC) by machine learning (ML) agents. Artificial Intelligence (AI) trained by Reinforcement learning algorithms can adapt to the game environment and act based on users’ actions or changes in surroundings. This research project aims to test the idea of replacing the NPC by artificial intelligence agent trained by Reinforcement learning algorithms. The data for this project was collected by observation of experiments inside of the game created in Unity platform. Agent was trained in special toolkit for Unity platform called "Unity ML-Agents". The behavior of the agents was subjected to preparatory measures for replication, including preliminary data processing using behavior cloning methods. The selected algorithm of the Reinforcement learning was the Proximal Policy Optimization, which is based on balance between new actions and its rewards. The training progress of the agents was monitored utilizing TensorBoard, the default package within the Unity ML Agents toolkit. During the research initialization phase, there were set null hypothesis, which states that pre-scripted bots are better for games and alternative hypothesis, which states that AI can replace NPC and enhance the game experience for a player.
Keywords: Game;Gameplay Experience;Reinforcement Learning;Unity;ML Agents;Non-playable character (NPC);Artificial Intelligence (AI)
Title: Mobile Application of Convolutional Neural Networks for Melanoma Classification
Abstract: Skin cancer is one of the most common types of cancer in terms of incidence around the world. Melanoma, in particular, manifests as visible pigmented skin lesions, often a result of sun overexposure. Despite the fact of rapid development of dermatology, delays in diagnosing skin diseases and poor access to their treatment are occurring in modern medicine.Several studies have demonstrated the potential of CNN in developing accurate and noninvasive screening methods for skin cancer. Nowadays, a limited number of mobile applications are identified that involve deep learning for the scanning and screening of skin diseases.This study simultaneously proposes a CNN architecture with a non-standard activation function, as well as presents the results of the proposed CNN model in terms of various metrics, such as accuracy, recall, specificity, precision, and so on. The software development is implemented in the Flutter framework, focusing on the Waterfall model. The key components of the mobile application, including the splash screen, main page, navigation, health-related features, and the "About Us" section, are described.The results of this study demonstrate that the proposed CNN model achieved 81.53% in terms of accuracy. The image augmentation techniques employed contribute to improved model generalization and robustness. By combining the capabilities of CNNs with user-friendly mobile interfaces, individuals can gain easy access to melanoma assessment, promoting proactive healthcare practices.
Keywords: Mobile application prototype;skin cancer;CNN;melanoma classification;Flutter;dataset;image augmentation;training;validation;accuracy
Review score: Moderate
Review comment: Article on a current topic. The article corresponds to the topics and requirements of the conference. The main results are supported by examples, the illustrations are clear and should certainly help the reader. The presentation of the solution to the problem is quite logical and consistent.
Title: Hybrid Deep Neural Network for Face Liveness Detection in Real-Time Video
Abstract: This paper presents a novel Hybrid Deep Neural Network approach for enhancing face liveness detection in video sequences, a critical component in safeguarding biometric authentication systems against sophisticated spoofing attacks. Leveraging the strengths of convolutional neural networks for spatial and frequency domain feature extraction the proposed model offers a comprehensive solution to detect and differentiate between live and spoofed face presentations. Utilizing the OULU-NPU dataset, featuring real-world variations and diverse spoofing scenarios, our model was rigorously evaluated across multiple performance metrics, including accuracy, precision, recall, and F1-score. The research introduces dynamic thresholding and Grad-CAM visualizations to improve decision-making flexibility and model interpretability, respectively. The results demonstrate the HDNN model's significant performance and adaptability to various attack vectors and environmental conditions, making a significant contribution to the field of biometric security. In addition to advancing face liveness detection technologies, this work paves the way for future investigations into biometric identification systems that are more reliable and effective.
Keywords: liveness detection;deep learning;machine learning;face liveness;image processing;computer vision
Review score: Moderate
Review comment: The paper on enhancing face liveness detection using a novel Hybrid Deep Neural Network (HDNN) approach represents a work in the realm of biometric authentication security. By integrating convolutional neural networks (CNNs) for both spatial and frequency domain feature extraction, the proposed HDNN model addresses a crucial challenge in biometric security systems: the differentiation between live and spoofed face presentations. This challenge is paramount in protecting against increasingly sophisticated spoofing attacks that threaten the integrity of biometric authentication systems.

The thorough evaluation of the HDNN model using the OULU-NPU dataset, which includes a wide range of real-world variations and diverse spoofing scenarios, highlights the rigorous and comprehensive nature of the research. The performance metrics employed—accuracy, precision, recall, and F1-score—illustrate the model's effectiveness and its superiority in identifying spoofing attempts under various conditions. Furthermore, the introduction of dynamic thresholding and Grad-CAM visualizations is commendable, as these advancements not only enhance the flexibility in decision-making but also improve model interpretability, facilitating a deeper understanding of the detection process and contributing to the development of more secure systems.
Title: Hybrid Deep Neural Network for Face Liveness Detection in Real-Time Video
Abstract: This paper presents a novel Hybrid Deep Neural Network approach for enhancing face liveness detection in video sequences, a critical component in safeguarding biometric authentication systems against sophisticated spoofing attacks. Leveraging the strengths of convolutional neural networks for spatial and frequency domain feature extraction the proposed model offers a comprehensive solution to detect and differentiate between live and spoofed face presentations. Utilizing the OULU-NPU dataset, featuring real-world variations and diverse spoofing scenarios, our model was rigorously evaluated across multiple performance metrics, including accuracy, precision, recall, and F1-score. The research introduces dynamic thresholding and Grad-CAM visualizations to improve decision-making flexibility and model interpretability, respectively. The results demonstrate the HDNN model's significant performance and adaptability to various attack vectors and environmental conditions, making a significant contribution to the field of biometric security. In addition to advancing face liveness detection technologies, this work paves the way for future investigations into biometric identification systems that are more reliable and effective.
Keywords: liveness detection;deep learning;machine learning;face liveness;image processing;computer vision
Review score: Moderate
Review comment: The author is recommended to remove words such superior,  innovative, valuable insight from the conclusion, which may indicate the active use of generative LLM. The model developed by the author solves the problem at an acceptable level, however, its superior perfection must be proven on a large dataset and using multi-criteria experiments.
Title: Assessing Electoral Integrity: Paillier’s Partial Homomorphic Encryption in E-Voting System
Abstract: In today’s digital age, electronic voting systems are at the forefront of advancing democratic practices. This study delves into the use of the Paillier algorithm’s Partial Homomorphic Encryption (PHE) properties within an electronic voting framework. With a strong emphasis on safeguarding data security and ensuring voter anonymity, the proposed model upholds the integrity and confidentiality of the voting process. The implementation of the Paillier cryptosystem in Python offers a secure, efficient, and scalable solution for e-voting. A thorough comparison with traditional and other homomorphic encryption methods highlights the superior security and computational efficiency of our approach, making it a promising avenue for enhancing the electoral process.
Keywords: Electronic Voting System;Partial Homomorphic Encryption;Paillier’s Algorithm;Python;Data Security;Voter Privacy
Title: Simulation of motion control of a cylindrical robot manipulator
Abstract: In this paper, a dynamic model of a cylindrical robot manipulator of RPP (Revolute-Prismatic-Prismatic) type based on classical laws of mechanics including Euler-Newton, Kane and Lagrange approaches is developed and presented. MATLAB Robotics Toolbox tools are used to demonstrate and analyze the manipulator working environment. The main model parameters include generalized coordinate vector (q), generalized velocity vector (dt/dq) and generalized acceleration vector (dt2/dq), which provides a comprehensive approach to controlling the robot dynamics. The presented code is characterized by simplicity and high efficiency in the implementation of robot manipulator control. A key feature of the model is the ability to specify user-defined points, which allows real-time tracking and analysis of the manipulator trajectory, ensuring its adaptability and flexibility under different operating conditions.
Keywords: cylindrical robot manipulator;control of the dynamics of a robotic manipulator;modeling of motion trajectories
Review score: Moderate
Review comment: 1) The abstract is not properly mapped with the results shown in the paper.
2) Section No. IV and V are missing in the manuscript.
3) Section III RESULTS is not written as per IEEE Format.
4) Acknowledgement section needs to be added in the manuscript, as per IEEE Format requirements.
5) A very less number of papers are added in the reference section. More papers need to be added and cited in the manuscript.
6) Grammatical errors need to be removed from the whole manuscript.
7) Tables are not given any title or header. Need to write it in the manuscript.
8) Paper doesn’t match with the theme of the conference.