bluuebunny/crossref_metadata_2025 huggingface dataset:
AdaGCR: An improved method for optimizing machine learning training process
Authors: Hu Mingtao
Published: March 2024

In contemporary machine learning, training datasets are typically divided into batches, and models are updated incrementally through batch iterations to save memory and reduce overfitting. However, determining the optimal hyperparameters like learning rate, batch size and number of epochs remains a challenge which often relying on empirical insights. This paper explores a novel method called Adaptive Gradient Conflict Rate (AdaGCR) to optimize the training process. It leverages the idea of gradient conflict rate, which reflects the models position within a batch model set and accordingly adjusts the global learning rate. This proposed method is tested by training a Deep Neural Network (DNN) model with MNIST dataset which represents simple tasks and a ResNet-18 model with CIFAR-10 dataset which represents more complicated tasks for solving real world problems. Experiments conducted on DNN demonstrates the proposed methods effectiveness in reducing overfitting and enhancing convergence, particularly with a well-suited initial learning rate. However, its applicability to more complex models like ResNet-18 may require further refinements, such as layer-specific learning rate adjustments. Future research should focus on fine-tuning AdaGCR and extending its utility across diverse machine learning models and tasks.
Exploration of hyperparameter efficiency for image style transfer
Authors: Meng Qingxin
Published: March 2024

Image style transfer is a popular computer vision technique that aims to merge the content of one image with the style of another to generate a unique, original image with a different aesthetic feel. Numerous models have been developed for various applications in this field, including portrait painting, art creation, and medical image processing, where additional information or annotations could be added to medical images, making them easier to read and understand. This study focuses on optimizing parameters within the pre-trained Visual Geometry Group (VGG19) network architecture, building on Google Brains 2017 work on Arbitrary style transfer in one model. The goal is to improve the quality and realism of the generated images by exploring different parameter combinations and fine-tuning weights and learning rates. This work carefully selects a range of styles and content to compare their effects during the optimization process. Finally, this fine-tuning process strikes a balance between content loss and style loss, which results in high-quality and more realistic images.
Financial assets prediction based on ARIMA, Random Forest and GRU
Authors: Li Yiming
Published: March 2024

As a matter of fact, financial asset prediction is a domain of great interest because of its potential to generate revenue. In reality, financial asset prediction models have evolved from basic time series analysis models to contemporarily hybrid models with the help of machine learning algorithms. To be specific, this study will introduce and analyse three popular financial asset forecasting models and their hybrids in terms of the properties they have demonstrated in the completed studies. In reality, good results have been achieved using a time series analysis method named autoregressive integrated moving average (ARIMA) for capturing linear elements. According to the analysis, in dealing with data noise and interpretability, Random Forest (RF) algorithm, a machine learning technique, produced positive outcomes. Deep learning technique gate recurrent unit (GRU) produced positive outcomes in terms of prediction accuracy. Based on the evaluations, this study indicates future research directions in the field of financial asset forecasting by analysing and organizing the characteristics of three different mainstream models.
The influence of Graphical User Interfaces on human-computer interaction and the impact of organizing software on decision-making process
Authors: Peng Shurong
Published: March 2024

The necessity for effective information management has grown crucial due to the widespread availability and utilization of electronic devices and software systems. Organizational software provides a solution by enhancing efficiency and reducing cognitive burden. Graphical User Interfaces (GUIs) play a crucial role in facilitating the interaction between humans and computers in the domain of software design. This study employs a comprehensive literature review to examine the fundamental concepts underlying efficient GUI design and the current trends in organizing software interfaces. The text presents a GUI concept that has been designed for the purpose of organizing software in order to address complex decision-making scenarios. The model offers a graphical representation of a framework that may be used to analyze various elements influencing decision-making. This study sheds light on the potential of Organizing Software to enhance cognitive processes. However, there is a need for additional enhancements in integrating user connectivity and intelligent algorithms. In general, the study emphasizes the significance of user-centric GUI design in improving the effectiveness of Organizing Software.
Self-supervised text de-stylization based on BERT
Authors: Huang Junyang , Lin Xiaoxiao
Published: March 2024

Recent advancements in Natural Language Processing (NLP) have ushered in a new era of textual style transfer (TST), a domain aimed at altering textual attributes such as tone and sentiment while preserving the content's essence. This study introduces a creative framework that employs a dual-component architecture consisting of a classifier and a generator to achieve text de-stylization, particularly sentiment neutralization. The classifier, built upon the Bidirectional Encoder Representations from Transformers (BERT) model, serves as a dynamic loss function guiding the generator, constructed on a Transformer-based encoder-decoder framework, to produce sentiment-neutral text. Our method leverages a self-supervised mechanism, enabling the generation of target text without reliance on parallel corpora, thereby addressing the limitations of existing TST methodologies. We preprocessed datasets from Stanford Sentiment Treebank-5 (SST-5) and Internet Movie Database (IMDb) movie reviews and employed them for training the classifier and generator, respectively. Preliminary results demonstrate the model's proficiency in preserving semantic integrity while effectively neutralizing sentiment. Future work envisions expanding this framework to enable text stylization across a spectrum of discursive contexts, enhanced by deep learning architectures and an iterative feedback mechanism for user-driven refinement.
An overview of Graph Convolutional Networks in skeleton-based action recognition
Authors: Yan Jin
Published: March 2024

The field of research is currently focused on human activity recognition. Hence, numerous pertinent literature reviews have expounded upon the multifaceted nature of data, the process of selecting feature vectors, and the advantages and disadvantages of classification networks. Graph Convolutional Networks (GCNs) have demonstrated significant efficacy in the domain of human action recognition. In recent years, with the rapid development of 3D skeleton data collection, a plethora of studies in action recognition based on skeleton data have emerged. Skeleton data consists of three-dimensional coordinates of multiple spatiotemporal skeletal joints, making it an effective representation of kinematics. It can be easily acquired through low-cost depth sensors and also directly extracted from two-dimensional images using video-based pose estimation algorithms, attracting widespread attention. As relational networks continue to evolve, GCNs have been applied to various fields, including human action recognition. GCNs have demonstrated significant advantages in feature extraction from skeleton data. However, using GCNs alone may have various limitations. Therefore, in recent years, many enhancement measures for GCNs have emerged. This review aims to summarize the research achievements of Graph Convolutional Network improvements in the field of human action recognition in recent years. It intends to assist future researchers in quickly organizing their research ideas, facilitating the generation of new results.
Detecting concentration of Ozone based on different machine learning methods
Authors: Yu Chihao
Published: March 2024

Hundreds of years ago, the environment was not that polluted; however, as the industrial revolution came, although our life became much easier, many toxic substances were released. These toxic substances result in Ozone pollution. Thus, the topic of the research focuses on predicting the amount of ozone present in the atmosphere in some areas of the world and how different factors, like environmental issues, can affect the pollution of Ozone. Moreover, which factors contribute the most. Through the extensive literature review and retrieval, it raised the question of how different methods of detecting concentrations of Ozone have drawbacks and advantages. In short, the main conclusion is that the simple methods (KNN and Perceptron) we are familiar with are enough to produce good enough results, and though these two simple methods have some disadvantages, there are more advanced methods (GBRT) that make the results even better.
Research report on automatic unpacking operation of measuring instrument storage buffer area
Authors: Xu Yongjin , Lv Miaoli , Lu Ran , Liu Gongjun , Ma Yuwei , Shi Bowen , Li Yue
Published: March 2024

In order to realize the automatic, digital and intelligent research and development of the key process of unpacking, this paper designs an intelligent unpacking equipment based on visualization of the makeup and unloading system, and the efficiency is seriously not matched. intelligent unpacking equipment based on visual positioning algorithm, including the main frame of the unpacking machine, the film winding device, the automatic film cutting and the film clamping. automatic film cutting and the film clamping mechanism.
Based on the above design, the test simulation is carried out. In the actual work, the intelligent unpacking workstation can significantly improve the current manual unpacking work efficiency (increase efficiency > 50%), and realize the automation, digitalization and intelligence of the current manual unpacking work efficiency (increase efficiency > 50%), and realize the automation, digitalization and intelligence of the operation process in the front area of the library. In the actual work, the intelligent unpacking workstation can significantly improve the current manual unpacking work efficiency (increase efficiency > 50%, and realize the automation, digitalization and intelligence of the operation process in the front area of the library.
Review of method name recommendation
Authors: Zhang Hui , Luo Zhenjie
Published: March 2024

In software engineering projects, method name recommendation is a critical task aimed at assisting developers in selecting appropriate method names to enhance code readability and maintainability. Given the significance of method name recommendation, numerous researchers have delved into improving the quality of method names. In this paper, we systematically review the existing method name recommendation techniques and conduct a comprehensive analysis of their principles, characteristics, and performance. Furthermore, we summarize the comparisons and evaluations among various methods to aid researchers in choosing method name recommendation techniques that suit their specific requirements. Lastly, we highlight the future research directions in this field. Through this review, readers will gain a clearer understanding of the current state of method name recommendation, providing valuable guidance for further research and development.
Research on the prediction method of first-day box office considering the holiday factor
Authors: Kuang Xi
Published: March 2024

The prediction of first-day box office has been a hot research topic. However, existing research on movie prediction models lacks analysis and exploration of the holiday factor. In order to uncover the holiday factor contributing to the fluctuation of movie box office, we propose a prediction method for the first-day box office considering the Holiday Factor. Initially, we present a SARIMABox-Cox model for time series analysis. The experimental results reveal significant differences in movie box office during weekends and weekdays, as well as during summer vacation and non-summer vacation periods. Consequently, we introduce two variables, weekend dummy variable and summer vacation dummy variable, as holiday factors. Subsequently, to further investigate the impact of the holiday factor on the accuracy of first-day box office prediction, we integrate the holiday factor with multiple linear regression (MLR), random forest (RF), support vector machine regression (SVR), deep neural network (DNN), and extreme gradient boosting (XGBoost) to establish several first-day box office prediction models. Experimental results on the publicly available dataset Daily Earnings Data for 3400 Movies indicate a substantial improvement in the performance of models considering the holiday factor, with the highest enhancement reaching 16.42%.
QR code resolution improvement based on Super-Resolution Generative Adversarial Network
Authors: Du Menghan
Published: March 2024

QR codes have become an integral part of our daily routines, simplifying tasks ranging from accessing websites to making payments. However, the quality of QR codes, especially their resolution, can significantly impact their functionality. Low-resolution QR codes may lead to misinterpretation during scanning and even decoding failures. To address this issue, researchers have explored various techniques to enhance the resolution of QR codes. Traditional image processing methods, such as interpolation and filtering, have been used in the past for resolution enhancement. However, these methods often result in overly blurry images with poor perceptual quality. Conversely, solutions based on Convolutional Neural Networks (CNNs) can introduce clarity but may compromise the sharpness of image edges. This paper presents an effective approach to improve QR code resolution using a Super-Resolution Generative Adversarial Network (SRGAN). The results are impressive, with SRGAN achieving a Peak Signal-to-Noise Ratio (PSNR) of 30.06, significantly outperforming the 17.48 achieved by the SRCNN method. Additionally, in terms of Structural Similarity Index (SSIM), SRGAN reaches 0.936, surpassing SRCNN's 0.473. These metrics demonstrate that SRGAN is highly effective in enhancing the resolution of QR codes, ensuring better scan accuracy and overall functionality in practical applications.
Medical image super-resolution reconstruction: A comprehensive investigation of Generative Adversarial Networks
Authors: Bao Yuntian
Published: March 2024

Medical images play a crucial role in modern healthcare diagnostics and treatment. However, many medical images suffer from limitations in resolution, potentially impeding a comprehensive understanding of a patient's condition by healthcare professionals. This comprehensive review delves into the applications of Generative Adversarial Networks (GANs) in medical image super-resolution reconstruction to address this challenge. In the Methods section, this paper first focused on the direction of medical image classification, including cell classification of histopathological images and synthetic data enhancement using GANs to improve liver lesion classification. Subsequently, this paper focused on the direction of medical image segmentation, looking into the use of Structure-Corrected Adversarial Networks (SCAN) for organ segmentation in chest radiographs and Deep Adversarial Networks for biomedical image segmentation using unannotated images. In the Applications and Discussion section, this paper thoroughly examined the current progress of GANs in telemedicine diagnosis and disease state generation and prediction. This paper emphasized the significant potential of GAN technology in telemedicine while outlining the current constraints and challenges. Furthermore, this paper highlighted the prospects of GANs in medical image super-resolution reconstruction and how they affect the discipline of medical imaging. This comprehensive review consolidates the latest research findings on GANs in medical image super-resolution reconstruction, underscoring their importance in the realm of healthcare. By critically analysing existing literature, this paper provides valuable insights for medical image analysts are researchers while inspiring future research directions and innovations.
An improved focal loss for imbalanced network traffic classification
Authors: Yu Songnan
Published: March 2024

With the continued progress of the Internet, network security has become an increasingly significant issue that requires constant attention and research. Network traffic classification is a key technology used to detect and prevent malicious network activity, and it has accordingly received increasing attention and research. However, datasets related to malicious network traffic classification often have imbalanced characteristics. In conventional traffic classification problems with multiple categories, the sample size characteristics of small categories are often overlooked. To address this issue, the focal loss function was proposed, which focuses on small samples by modulating the trade-off between the positive and negative samples through two hyperparameters and . This article uses convolutional neural networks (CNN) to tackle traffic classification problem and explore the optimal values of parameters in this application scenario. Additionally, this work proposed a novel weight allocation formula to replace , which allowed small class traffic to obtain higher accuracy.
Research on the application of machine learning in business analytics: Cases of Amazon and eBay
Authors: Zhang Rongrong
Published: March 2024

With the rapid development of the Internet and the rise of e-commerce, commercial enterprises are faced with a large amount of data and a complex market environment. In this situation, machine learning, as a powerful tool, is widely used in the field of business analysis. In this dissertation, we take Amazon and eBay as examples to study the application of machine learning in the company's business analytics, focusing on its role in market prediction, customer behavior analysis and operation optimization. By analyzing the relevant cases, we find that machine learning technology plays an important role in helping companies make more accurate decisions and improve efficiency. Studying the application of Amazon machine learning in business analytics can promote in-depth research on the application of machine learning in business in academia, and promote the application and development of machine learning technology in other business scenarios. Overall, the application of machine learning in business analytics can help companies understand customer behavior, optimize operations, and improve sales results. However, there are still some challenges, such as data quality, algorithm selection and privacy protection. Therefore, further research and innovation are necessary to advance the development of machine learning applications in business analytics.
Research on method of the data classification with an example
Authors: Yang Yue
Published: March 2024

The huge data from real life is a problem amount the researcher. So, it is important to let us divide them in out project. This article wants to talk about the way of data classification. The article using different method to shows how these methods works, start from the simple word dividing and a little complex method which using the root to be the key word. Then the article shows the complex PCA or k-mean dividing which is always appear with the mechanize learning. Then the article using a true example to shows, how this different way of dividing the data can work for different situation. This article can truly increase the speed of the researcher work and increasing the accuracy of the data analysis by letting the data to be more methodical.
Credit card transaction fraud detection based on machine learning
Authors: Jiang Yuxin
Published: March 2024

The rise of e-commerce payment systems has been swift due to our society's rapid advancement. However, the transparency and vulnerability of the internet have opened new avenues for illicit access, resulting in a significant surge in financial fraud cases, particularly credit card transaction fraud. Detecting and mitigating such incidents has become crucial. One branch of artificial intelligence, machine learning, offers a potent solution. It operates by utilizing various algorithms and models that rely on established patterns and reasoning without explicit instructions. By processing vast amounts of historical data, machine learning models can identify underlying data relationships, allowing them to make accurate predictions based on input data. Therefore, it emerges as a highly effective method for credit card transaction fraud detection. This paper reviews the research methods for credit card fraud, introduces credit card transaction fraud detection data sets, and outlines machine learning algorithms and models related to credit card fraud detection. It also considers the future prospects for machine learning development and possible challenges.
Comparative analysis of different model depths on convolutional neural network for handwritten digit recognition
Authors: Deng Weitao
Published: March 2024

In the rapidly advancing field of artificial intelligence, Convolutional Neural Networks (CNNs), as a representative method, have risen to prominence as a pivotal instrument for handling visual data. However, despite their widespread use, the impact of CNN depth on performance remains under-explored. This study delves into this aspect, evaluating the performance of CNN architectures with different depths - two-layer, four-layer, and five-layer - on the MNIST dataset, a version from the National Institute of Standards and Technology, a well-known benchmark dataset for handwritten digit recognition. Experimental results reveal that the four-layer model achieved the highest average accuracy of 99.76%, while the five-layer model, despite its additional complexity, only slightly trailed behind with a 99.73% accuracy rate. However, the five-layer model required a significantly longer training time. In conclusion, while deeper networks can increase accuracy, they can also introduce computational inefficiencies without significant gains in performance. This research provides a better understanding of CNN depth, guiding optimal model selection for image classification tasks.
AI in education: Enhancing learning experiences and student outcomes
Authors: Xu Zhiyi
Published: March 2024

This research article makes an attempt to investigate the potential of Artificial Intelligence (AI) in enhancing the learning experiences, as well as student outcomes. As a result, it has developed a study that will be able to understand how the different AI tools, including machine learning, data learning, virtual reality (VR) and augmented reality (AR), automation, and so forth can be used to develop learning experiences and outcomes. Subsequently, a case study involving a mathematics classroom was used to collect data and confirm whether indeed AI led to improved learning experiences and study outcomes. The study confirms that AI resulted in positive outcome with positive performance measure sin academic performance, motivation and engagement, learning progression, and so forth.
Ancient Chinese painting style transfer based on CycleGAN
Authors: Bai Jianwei
Published: March 2024

Style transfer aims to alter the visual aesthetic of images by giving them a different artistic style. With the rapid advancement of deep learning, style transfer tasks have made significant progress, introducing new perspectives and innovative potentials within the realm of image processing. This study seeks to explore style transfer methods based on Cycle-Consistent Adversarial Networks (CycleGAN), enabling contemporary landscape photographs to take on the form of ancient Chinese paintings. This endeavor opens up fresh possibilities for artistic creation, image editing and design applications. The research encompasses an exposition of the process involved in constructing the CycleGAN model, alongside presenting research findings. Furthermore, it delves into the discussion of crucial techniques employed during the model training process, specifically the utilization of cycle consistency loss in configuring the loss functions. Lastly, this study ventures into future research directions, including strategies for further enhancing the performance and expanding the application scope of this style transfer model.
Revolutionizing machine learning: A comprehensive analysis of ASIC hardware accelerators and their applications
Authors: Lei Weiming
Published: March 2024

The growth of the web over the past few years has led to tremendous data growth, which has provided a powerful impetus for artificial intelligence and machine learning. Machine learning algorithms are widely used in various classification and prediction problems. However, with the rich data types and needs, the traditional software computing method that relies on CPU can no longer meet the application scenarios under different requirements. Machine Learning (ML) hardware accelerators, especially Application Specific Integrated Circuits (ASICs), have become trendy to meet a variety of needs. This paper reviews the research on ML ASIC in the past few years, reviews the development of ML and ASIC design, and summarizes the characteristics of their use. It is followed by examples of various scenarios for which it can be used, such as medical diagnostics and internet of thing (IOT) terminals. Finally, the author analyses the existing problems and limitations, and gives the improvement methods of hardware and algorithm to deal with the related obstacles. It is hoped that this paper can provide some help and convenience for the subsequent related research.
A comparative study of FPGA and CGRA technologies in hardware acceleration for deep learning
Authors: Luo Jiawei
Published: March 2024

With the rapid growth of deep neural networks, their increasing demand for computing resources poses significant challenges to resource-constrained edge devices. Field Programmable Gate Array (FPGA) and Coarse-Grained Reconfigurable Arrays (CGRA) have emerged as promising solutions due to their parallelism and energy efficiency advantages. This paper conducts a comprehensive comparative analysis of FPGA and CGRA for accelerating deep learning workloads. It begins by introducing convolutional neural network architecture and optimization techniques. Various dataflow strategies are discussed for memory access optimization. The paper then analyzes representative cases to clarify FPGA and CGRA architectural advantages, along with optimization techniques through different dataflow strategies. The comparison reveals that FPGA offers higher throughput with fine-grained programmability for flexible bit-level operations and dataflow control. In contrast, CGRA excels in energy efficiency and area consumption with coarse-grained custom processing units and optimized on-chip interconnection networks. Quantitative comparisons are provided for throughput, energy efficiency, and area occupation. These insights can serve as a reference for selecting the appropriate architecture when designing custom hardware accelerators for deep learning workloads.
Resilience and recovery: Analyzing the economic impact of COVID-19 on Beijing's catering industry
Authors: Chen Jiayi
Published: March 2024

The COVID-19 pandemic, which emerged in late 2019, had far-reaching economic consequences, impacting industries worldwide. This study examines the specific case of Beijings catering industry to understand how it undergoes the pandemics challenges and what lies ahead. Using quarterly revenue data from 2017 to 2022 sourced from the Beijing Municipal Bureau of Statistics, an ARIMA model is employed for analysis. The model not only reveals the pandemics impact on revenue but also provides forecasts for the future. In 2022, quarterly analyses illustrate a complex narrative of the industrys resilience. Revenue fluctuations reflect the influence of government policies, highlighting the need for businesses to remain adaptable and monitor policy changes closely. The predictive ARIMA model suggests relative stability for the catering industry in the coming quarters, serving as a useful reference for strategic planning. However, external factors such as market dynamics and policy shifts must be considered. Furthermore, actionable recommendations for catering businesses are offered. These include remaining agile in response to policy changes, investing in employee training and development, and embracing digital marketing strategies.
Detection of anomalous ticket purchasing behavior for concerts based on machine learning
Authors: Lingyang Zeng , Lilin Peng
Published: March 2024

In recent years, there has been a continuous increase in the demand for entertainment activities. With the changing policies related to epidemic prevention and control, consumers ticket demands for various entertainment events, particularly concerts, have seen a significant rise. This has led to the emergence of anomalous ticket purchasing behaviors, including ticket scalping software, bulk purchasing, and fraudulent transactions. Such behaviors not only infringe upon the rights of legitimate audiences but also harm the interests of concert organizers. Therefore, the detection and prevention of anomalous ticket purchasing behaviors for concerts have become essential. This paper focuses on the use of automated systems, robots, or malicious software for ticket purchases, limiting consumers participation in abnormal ticket-buying activities. Combining previous research on railway ticketing systems and real-life experiences, this study selects ticket-purchasing frequency, speed, quantity, seat selection, payment method, IP address changes, and historical ticket-purchasing records as feature values. Different machine learning models are chosen for different feature values to conceptualize the construction of an anomalous ticket purchasing system for concerts. This paper emphasizes the importance of detecting anomalous concert ticket purchasing behavior, provides different machine learning models for various feature values, and lays the foundation for building an overall anomaly detection system. This research serves as a crucial reference for the security and user experience of ticketing systems, aiming to continually improve and upgrade anomaly detection systems to adapt to evolving challenges. The hope is to enhance the ticket-buying experience for future concerts and large-scale events.
Cryptocurrency assets valuation based on statistics model and machine learning
Authors: Hao Congyue
Published: March 2024

As a matter of fact, cryptocurrency is an exchange media, which uses cryptographic functions to trade, which achieves decentralisation, transparency, and invariance using blockchain. With the rapid development since the first white book proposed in 2008. The market value of cryptocurrency has been exponentially expanded rapidly in recent years. On this basis, programmatic trading strategy are paid more attention by people in trading cryptocurrency contemporarily. With this in mind, this study provides three models, that is, Exponential Smoothing Model (ESM), Autoregressive Integrated Moving Average (ARIMA) model and Long short-term memory (LSTM) model to forecast the future price of cryptocurrency and use sum of square error to analyse the results of these prediction. According to the analysis, LSTM is the best model to forecast the price of cryptocurrency in this article. Overall, machine learning might be more useful to forecast in this fields than traditional statistical models. Investors and researchers in the future could continue to forecast the price of cryptocurrency combined with LSTM model.
Personalized federated learning for dealing with computational latency and non-IID data scenarios
Authors: Chen Mengnan , Wei Yaya , Zhong Ziyan
Published: March 2024

Federated learning (FL) is widely used because it is effective at enhancing data privacy. However, there will be many problems in the FL training process, such as poor performance of training models and the model converging too slowly, as the data is typically heterogeneous and the computing capabilities of the participants device are different. Here, we proposed an optimized FL model paradigm, that applies model arithmetic prediction to prevent the training process's inefficiency due to the participants' limited computational resources. The proposed formula for participant selection is based on posterior probabilities and correlation coefficients, which have been validated to reduce data noise and enhance the effect of central model aggregation. In addition, high-quality participant models are selected based on posterior probability, combined with correlation coefficients, which allows the server model to aggregate as many better-performing participant models as possible, meanwhile avoiding the impact of participants with too much data noise. During the aggregation step, the model loss values and the participant training delay are used to weight factors for participant devices, which accelerates FL convergence and improves model performance. Data heterogeneity and non-IID are fully taken into consideration in the method we proposed. Finally, these results have been verified by extensive experimental, we demonstrate better performance in the presence of non-IID data, especially affective computing. Compared with previous research, reduces training latency by 4 seconds, and the model accuracy is increased by 10% on average.
Comparison and analysis of the accuracy of Lasso regression, Ridge regression and Elastic Net regression models in predicting students' teaching quality achievement
Authors: Ren Pinguang
Published: March 2024

In this paper, the "Student Performance Data Set" data set of Kaggle competition is used to conduct correlation analysis on multiple attributes such as students' personal information, school information and students' school performance, and a correlation heat map is drawn. The results show that there is a strong positive correlation among the attributes. We then divided the data set into training, validation, and test sets in a 6:2:2 ratio, and used the Lasso regression model, the Elastic Net model, and the Ridge regression model to make predictions. After training 50 epochs, we evaluated and compared the models. The results show that Lasso regression model has the lowest prediction error and the best error effect. Elastic Net was the next best predictor, while Ridge regression model had the largest prediction error and was the worst. To sum up, Lasso regression model has the best Performance in grade prediction based on the "Student Performance Data Set" dataset. This conclusion is of great significance for schools and educational institutions, as it can help them better understand students' learning and improve the quality and effectiveness of teaching. At the same time, this conclusion is also valuable for data scientists and machine learning researchers, because it can guide them to choose the most appropriate model and algorithm on similar data sets, improving the accuracy and effectiveness of predictions. In general, this paper analyzes and discusses the problem of Student achievement prediction, puts forward the Lasso regression model based on the "Student Performance Data Set" data set, which has the best performance prediction effect, and analyzes the principles of the three models. This conclusion has practical applications for schools and educational institutions, as well as providing a reference for data scientists and machine learning researchers.
The Impact of COVID-19 Pandemic Linguistic Landscapes on Lifestyle, Health Awareness and Behavior
Authors: Alhazmi Albatool Ahmad
Published: March 2024

The recent COVID-19 pandemic created a plethora of new challenges for the world and affected all aspects of human life. This research aimed to look further into the sociolinguistic aspects of the COVID-19 Linguistic Landscape (LL) and assess the extent to which public signs affected people’s behaviors and lifestyles during the COVID-19 outbreak in the Saudi context. A semi-structured questionnaire was developed to collect data related to the study. A total of 215 participants from different regions of Saudi Arabia participated in the survey. The study results provide evidence of language as a critical element in reflecting the social realities of the Saudis. The data confirmed that the COVID-19 Linguistic Landscape (CLL) served several functions at both individual and institutional levels in Saudi Arabia. Key findings emerged about the role of the linguistic landscapes set up in public spaces in changing people’s thoughts and behavior as well as how they reacted to urgent and exceptional conditions such as COVID-19. In sum, the pandemic-associated signs led to remarkable positive changes in the daily routine of people.
Re-constructing Food Identities in Min Jin Lee’s Pachinko
Authors: . Vaishnavi , M Senguttuvan
Published: March 2024

Migration is often a result of forced escape from political or social uprisings, subordination faced on different levels or a free will to explore a nomadic lifestyle. The consequences faced range from cultural shock and alienation to acclimatizing to a foreign world. The basic elements that seek drastic changes are the food practices and traditions. Food is an integral part of a culture which can vary from culture to culture in terms of the style of cooking to the types of food consumed. This leads to a formation of the Self and the Other which the migrants find strenuous to grapple with. The paper Re-constructing Food Identities in Min Jin Lee’s Pachinko intends to navigate the lives of select characters in the chosen novel, that have paved their way in life through hybridized societies and hegemonic identities, along food cultures. It will study the clashes, confrontations and negotiations that finally lead to acculturation. It will also explore hybridization of indigenous culinary practices which create dissimilar yet known traditions that become difficult to comprehend and follow. Min Jin Lee’s Pachinko depicts a re-construction of food identities in different generations of wars and colonial occupations.
Exploring Academic Writing Needs and Challenges Experienced by ESL Learners: A Literature Review
Authors: S Harshalatha , Sreenivasulu Yadamala
Published: March 2024

The review paper aims to identify the challenges and academic writing needs faced by the students, specifically in English as a Second Language (ESL) contexts. The paper systematically reviews, analyzes, and summarizes relevant studies, categorizing the identified challenges and demands of students in academic writing. The central finding is that students often struggle with academic writing due to a lack of enthusiasm and insufficient practice in this skill. The paper emphasizes the formal writing style, scholarly voice, and intellectual values inherent in academic writing. The study employs a comprehensive approach, including a thorough review of the literature, an examination of academic papers and these as examples, and a detailed analysis of each paper’s components. The sections include an analysis of the title, an assessment of the abstract, and a description of the introductory section, covering issues, purpose, recommendations, and appendices, along with conclusions. Importantly, the review paper extends its focus beyond the university context, aiming to contribute to graduate students and society by addressing these challenges, which are crucial for enhancing academic writing proficiency and benefiting both students and society.
Sian Heder’s CODA and the Struggle for Artistic Freedom
Authors: Sumarsono Irwan , Suprihatien Suprihatien , Darmajanti Perwi , Lestari Chatarini Septi Ngudi , Hardiningsih Sri , Nuary Muhammad Guruh
Published: March 2024

This study analyzed the relationship between the main character’s artistic freedom and her complex family responsibilities as reflected in Sian Heder’s CODA. The writers focused on analyzing how Ruby, a young musician from a deaf family, handles her inner conflict between gaining her creative ambition and her social expectations and familial responsibilities. The writers analyzed how Ruby resolves the tension she faces and makes decisions and resolutions to pursue her artistic ambition. This study was expected to deepen our understanding of the complexities of balancing creative expression, family, and self-discovery. The writers got the main data from Sian Heder’s CODA, while the supporting data were derived from English literature journals, e-books, and other sources on the internet. The result shows that Ruby manages to handle her inner conflict, make decisions and resolutions to pursue her dream, and make her family believe that what she decides will bring betterment for her and her family.
Protection of Gully Slope in Loess Plateau
Authors: Lei Na , Dai Yilun
Published: March 2024

Rigid protection of traditional engineering slope protection measures can play a certain role in slope protection at the initial stage of the project, but with the passage of time, the slope will be affected by weathering, the stability of the slope will be reduced, in addition, a large area of engineering rock surface exposed will not be coordinated with the surrounding natural vegetation environment, which does not meet the requirements of sustainable development. Therefore, the ecological engineering restoration technology combining engineering measures and biological measures has become the trend and mainstream of slope management and restoration.
Fe3O4@SiO2 Magnetic Nanoparticles Modified with Cysteine: a New Adsorbent for Environmentally Friendly Adsorption/Desorption of Pb(II) ions
Authors: Bi Yuanjing , Sun Ningyan , Fan Ailin , Zhang Ming , Liu Xiangming , Xu Bo , Lan Guihong , Qiu Haiyan
Published: March 2024

In recent years, nanoparticles have become the focus of scientific research because of their wide potential application value. Iron oxides show incredible magnetic saturation, stability, and intuitive properties on the surface, making them ideal for a variety of uses. In this paper, nanometer iron oxide particles are synthesized by co-deposition method, and silica is further used to avoid agglomeration. Functional synthesis of cysteine nanoparticles (FNMs-Cys).The optimum synthesis conditions and adsorption conditions were determined by orthogonal experiments. In addition, the anti-interference and regeneration cycle performance of FNMs-Cys are discussed. The samples were characterized by scanning electron microscope (SEM), X-ray diffractometer (XRD), Fourier transform infrared spectrometer (FTIR), N2 adsorption and desorption automatic specific surface and porosity analyzer (BET), thermogravimetric analysis (TG) and hysteresis loop test (VSM). The adsorption process of Pb2+ by FNMs-Cys accords with quasi-second-order kinetics and Langmuir isothermal adsorption model, which shows that the adsorption is mainly through chemical adsorption of monolayer, In the Langmuir model, the maximum single-layer adsorption capacity of FNMs-Cys for Pb2+ reached 174.85 mg/g. After five cycles of adsorption-desorption regeneration, FNMs-Cys still showed good stability and recyclability. At present, it is of forward-looking significance to study the removal effect of Pb2+ in water by using amino acid modified mesoporous magnetic silica materials in laboratory.
Isolation and Characterization of a Novel Ochratoxin A- degrading Acinetobacter sp.strain 2-9B
Authors: Chen Ru , Li Dawei , Liu Yang
Published: March 2024

Ochratoxin A (OTA) is a polyketide mycotoxin that commonly contaminates agricultural products and causes significant economic losses. In this study, we investigated the OTA degradation ability of Acinetobacter sp. strain 2-9B which was isolated from the soil. The effects of temperature, incubation time, and pH were studied. Furthermore, it was examined whether the washed cell pellet and the culture supernatant of the fermented culture (SFC) of the bacterial cell components could detoxify OTA. The results showed that strain 2-9B, which achieved 100% degradation of 2.5 μg/mL OTA, was identified as Acinetobacter sp. strain 2-9B. It exhibited optimal degradation activity at 2.5 μg/mL under pH 6.0 and 51℃ incubation for 48 hours. The results also suggest that enzymes present in the supernatant of Acinetobacter sp. strain 2-9B were involved in OTA degradation. The biodegradation of OTA is a very feasible method for food and feed purification, as physical and chemical methods may not only eliminate mycotoxins but also nutrients and sensory-related compounds.
Research on the Environmental Protection Effect of Local Ecological Transfer Payment
Authors: Liu Yanan , Li Xianlong , Ma Yuanyi , Zhou Zejiong
Published: March 2024

Local ecological transfer payment refers to a policy tool used by the central government to provide financial support to local governments according to the ecological compensation mechanism for ecological protection. With the increasingly serious ecological and environmental problems in China, local ecological transfer payment has become an important way to protect the ecological environment and achieve sustainable development. The purpose of this paper is to explore the effect of local ecological transfer payment on environmental protection and analyze its impact on local economy and society.
Viacrozmerná analýza populačného starnutia v okresoch Slovenska v rokoch 2011 a 2021
Authors: Káčerová Marcela , Kusendová Dagmar , Stankovičová Iveta
Published: March 2024

Population ageing is a typical feature of population development in the majority of countries in the world. In each population, this process is specific – whether in terms of the timing of its onset or the factors that modify the process of population ageing. The main aim of this article is to identify the processes of population ageing in the districts of Slovakia. A regional analysis of this process focused on population ageing in Slovak districts in the years 2011 and 2021 using cluster analysis. The results of the cluster analysis of population ageing identified northern and eastern Slovakia as districts whose populations have a younger age structure. The western and southwestern districts of Slovakia have populations with an old age structure. In 2021, this age polarity began to change the territory of Slovakia. The suburbanised region of the capital, Bratislava, is becoming significant, as the population in the districts there is getting younger.