bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Application of Big Data Technology
Authors: Dong Suyi
Published: November 2024

The swift advancement of information technology has positioned big data as a crucial instrument for fostering innovation and enhancing optimization across diverse sectors. This essay seeks to investigate the specific applications and effects of big data technology in the financial, healthcare, and educational domains. The research background is that the wide application of big data technology has a profound impact on business processes and decision-making methods in various industries. Research topics cover big data for personalized learning and data parsing in education, disease prediction and personalized medicine in healthcare, and risk management and fraud detection in finance. This paper adopts the method of literature review and case analysis, and the research objects include educational platforms, medical institutions and financial institutions. The data comes from industry reports, academic studies and real-world cases. The findings indicate that big data technology has a noteworthy impact in improving service quality and decision-making efficiency, but concurrently faces issues like protecting data privacy, technical complexity and data quality. The conclusions point out that while big data technology is driving the industry, these challenges need to be addressed to achieve its maximum potential.
A Brief Review of Multi-modal Knowledge Graph Completion
Authors: Zhao Haoyue
Published: November 2024

Knowledge graphs have become the infrastructure of artificial intelligence. However, most current knowledge graphs are incomplete. Consequently, knowledge graph completion (KGC) has become a hot research topic. Researchers primarily focus on unimodal knowledge graph completion, which consists solely of textual information. With the rapid progress of AI, the demand for multi-modal knowledge graphs (MMKGs) is increasing. However, research on multi-modal knowledge graph completion (MMKGC) is still in its initial stages. There is no clear recognition of its status and trends. First, we summarize the multi-modal knowledge graph and its significance. Second, we classify the comparisons between unimodal knowledge graph completion and MMKGC. Finally, we discuss different methods of MMKGC. This paper may provide guidance for future research.
Depth-First Search and Research of Algorithms Related to Bipartite Graphs
Authors: Wang Jiayi
Published: November 2024

The application of bipartite graph is extensively used in many fields of daily production and life. This in turn led to a lot of research and thinking about the bipartite graph: what is a bipartite graph? How to tell if a graph is a bipartite graph? And related algorithms for some specific characteristics of a bipartite graph. This paper uses the literature research method and the experiment method, starting from the depth-first-search, particularly summarizes and clearly explains the related algorithms of the bipartite graph: the judgment of the bipartite graph, and the calculation of its maximum matching number. Lays a foundation for solving more problems that require the application of bipartite graphs.
Research on YOLOv3 Method Based on SE Module
Authors: Zhang Zi
Published: November 2024

Object detection is a crucial and challenging task in computer vision. With advancements in deep learning technology, YOLOv3 has become a widely adopted and efficient object detection algorithm. However, YOLOv3 encounters challenges when managing complex scenes and detecting small objects. To tackle these challenges, this research introduces an enhanced YOLOv3 architecture incorporating the Squeeze-and-Excitation (SE) module to improve feature representation capabilities. The SE module captures the interdependencies between channels and dynamically adjusts their feature responses, enhancing the models representation capability. By integrating the SE module into YOLOv3, this study seeks to substantially improve the models effectiveness in complex scenes and small object detection. Experimental results indicate that the enhanced YOLOv3 surpasses the original model on the COCO2017 dataset, validating the effectiveness of this method. Additionally, the improved architecture further enhances detection accuracy and robustness while maintaining efficient detection speed. The contribution of this study lies in proposing an effective feature enhancement method, introducing innovative concepts and techniques for object detection.
Representative Image Outpainting and Image Super-Resolution Methods Based on Deep Learning
Authors: Li Jike
Published: November 2024

The image generation based on deep learning is a technology that can generate new images or outpaint old images or improve visual effect of old images through learning from input data, according to deep learning structure. The representative technologies of image generation are image outpainting and image super-resolution. Deep learning is widely utilized in the field of computer vision. Facing different needs, it is essential to choose proper ways. This essay reviews several representative and new methods of image outpainting and image super-resolution. Compared with the results generated by old methods, the results generated by new image outpainting methods introduced in this essay have greater precision and clarity, the methods of image super-resolution that are suitable for all fields and professional fields can learn from each others dataset manually to train and develop. There is still unimaginable prospect of deep learning in the field of image outpainting and image super-resolution. Therefore, it is worthy of attention.
A Literature Review on Object Detection in Autonomous Driving
Authors: Yu Jingfeng
Published: November 2024

Object detection technology is a core component of autonomous driving systems, primarily tasked with the real-time identification and localization of objects in the surrounding environment, such as pedestrians, vehicles, and traffic signs. With the advancement of deep learning, various efficient object detection algorithms have emerged in recent years, such as the YOLO series, RetinaNet, and CenterNet. These algorithms have shown significant improvements in both processing speed and accuracy. However, the complexity and variability of autonomous driving environments still pose severe challenges to object detection. This review summarizes the current research status of object detection algorithms in autonomous driving, focusing specifically on the basic processes and characteristics of YOLO, RetinaNet, and CenterNet. It also discusses their improvements and optimization effects in practical applications. Additionally, this paper reveals the advantages and disadvantages of these three algorithms through a comparative analysis of experimental results, providing direction for future research. Finally, the paper discusses the main challenges faced by current object detection methods in the autonomous driving field and potential solutions, aiming to promote further development and application in this area.
A Review of Research on Object Detection Algorithms
Authors: Kong Xiangjin
Published: November 2024

Object detection is a fundamental task in computer vision, widely used in fields such as autonomous driving, security surveillance, medical imaging, and drone image analysis. With the continuous advancement of technology, object detection algorithms have evolved from traditional methods to deep learning approaches. This paper categorizes object detection algorithms into four types based on their technical characteristics and implementation methods: two-stage algorithms, one-stage algorithms, keypoint-based algorithms, and emerging Transformer-based methods. Through a performance comparison on existing datasets, it was found that two-stage algorithms excel in accuracy but consume significant computational resources, leading to slower speeds; one-stage algorithms have a clear advantage in speed but show lower accuracy in detecting small objects; keypoint-based methods effectively balance speed and accuracy; additionally, the emerging Transformer-based methods perform well in capturing global information but require large amounts of training data and computational resources. This paper summarizes the advantages and disadvantages of each type of algorithm and discusses future research directions.
A Review of YOLO-Based Target Detection Methods
Authors: Lyu Yunfan
Published: November 2024

Target detection is now a popular topic in computer vision. With the development and iteration of technology, deep learning is constantly emerging. The integration of deep learning in the target detection task has led to rapid improvement in accuracy and speed, among which the You Only Look Once (YOLO) series of methods have the most rapid and varied improvements and upgrades, which have been widely used in the fields of navigation, video surveillance, face detection, text detection, and aerospace, etc. This paper initially provides an overview of the research context, importance, and challenges associated with this domain, compares and analyzes the network structure and implementation of the single-phase target detection method represented by the YOLO series with the two-phase and other improved algorithms, and then introduces the research progress of the target detection algorithms of deep learning, the characteristics of the commonly used datasets, and the key parameters of the evaluation of performance indicators, and then presents a compilation of experimental outcomes associated with several widely recognized algorithms applied to prominent datasets. Subsequently, it enumerates the experimental findings of diverse algorithms on these established datasets. Ultimately, this paper anticipates future research trajectories and developmental trends pertaining to target detection algorithms.
A Review on Computer Vision-Based Methods for Abnormal Human Action Recognition
Authors: Yuan Mengdi
Published: November 2024

Human behavior recognition constitutes a crucial research domain within both computer vision and behavior recognition. As a branch of human behavior recognition, abnormal behavior recognition has witnessed rapid advancement in recent years, which is capable of enhancing the governance level of public safety. To investigate the theoretical and technical progress of abnormal behavior recognition in public places within the realm of computer vision, this paper initially delineates the definition of abnormal behavior in public places. Secondly, as computer vision and pattern recognition technologies have progressed, algorithms are now divided into two distinct categories: traditional methods and those leveraging deep learning techniques. The identification of atypical human behavior can be divided into two approaches depending on the detection of body key points: one approach uses skeletal key points, while the other focuses on analyzing temporal and spatial features. Finally, this paper conducts a review of the mainstream datasets of abnormal human behavior both at home and abroad, analyzes the performance of related algorithms on the datasets, and gives future research directions as well as optimization suggestions.
Review on Sensors and Path Planning Algorithms of Automatic Driving
Authors: Chen Jiayuan
Published: November 2024

automatic driving developed rapidly in recent years. Environment perception and path planning are two key functions in automatic driving, and sensors and algorithms are the basis for realizing these two functions. Through literature review and comparative analysis, this paper discusses sensors and path planning algorithms in automatic driving. An analysis is conducted on the principles, benefits, limitations, and applications of various sensor types in automatic driving. The RRT algorithm and A* algorithm are also discussed, as well as their advantages and constraints. Finally, this paper proposes suggestions such as deep learning, sensor fusion and cost reduction to update automatic driving. This paper provides a reference for the further development of automatic driving.
A Lightweight Deep-Learning Visual SLAM for Indoor Dynamic Environment Using Yolov10
Authors: Luo Ziye
Published: November 2024

Vision simultaneous localization and mapping (SLAM) technology has become a key research direction in the field of mobile robotics in recent years. However, the accuracy and stability of traditional vision SLAM technology greatly affected by the dynamic environment, and the mainstream dynamic feature point rejection method combining vision and semantic segmentation techniques is not applicable to edge-end devices with limited resources and high real-time requirements. This research suggests a visual SLAM algorithm based on the YOLOv10n lightweight target detection model and the GCNv2 feature point extraction model to accomplish real-time dynamic feature point rejection to address those problems. To compensate for the detection accuracy and stability issues of the YOLOv10n model while leveraging its real-time advantages, the algorithm also employs multi-target Kalman filtering with data association through the Hungarian algorithm, history window smoothing, and potential dynamic feature point recording methods to enhance robustness. The algorithm is validated on the TUM RGB-D Dataset, and the results demonstrate that the method can effectively reject the dynamic feature points in the dynamic environment, and it has a significant improvement in the accuracy and stability of the visual SLAM system.
Sensors and Path Planning Algorithms in Robots
Authors: Shao Jin
Published: November 2024

Robots are frequently used in many fields, such as agriculture, cleaning and supermarkets. These kinds of robots can help a lot in daily life, making lives much more convenient and efficient. Robots can also reduce peoples workload and keep them safe. Almost all robots nowadays use sensors and algorithms. Sensors and algorithms can make the robot move automatically. This paper mainly focuses on the three kinds of sensors, including the camera, lidar sensor, temperature sensor, and three types of path-planning algorithms, including Dijkstra, A*, and RRT algorithms. This paper concentrates on the benefits, drawbacks, principles, characteristics, limitations, and uses of each type of sensor and path-planning algorithm. Then, this paper primarily discusses the difficulties and problems the sensors encounter when path-planning algorithms combine with the sensors. Comparison of machine learning with traditional methods conducted after forwards. It is found that although the sensors and the robots' algorithms are widely used, there is still much room for improving the limitations and disadvantages. Finally, prospects for future research are presented and some suggestions are proposed.
Improving Similar Face Recognition Using ResNet-50 and FPN with Triplet Loss
Authors: Miao Xinbei
Published: November 2024

Identical twin recognition has been attracting attention as a challenge in face recognition. In this paper, the method of FPN combined with ResNet-50 and triplet loss function is used to achieve better recognition ability on similar face datasets including identical twins without affecting much the performance of ordinary face datasets. In order to illustrate the role of FPN, the author conducted a series of comparative experiments. With or without FPN as a variable, the experiment concluded that ResNet-50 combined with FPN enhanced the recognition ability of similar faces, and the author listed the reasons that may have led to this result. Subsequently, under the condition of different training parameter settings, before overfitting, the learning rate and the number of iterations were positively correlated with the performance of the model on the ordinary faces dataset and negatively correlated with that on the similar faces dataset (including identical twins). The performance of the model on the ordinary face dataset is negatively correlated with the performance on the similar face dataset. Finally, a relatively good set of parameters is decided among the three sets tested.
Recent Advances in Visual SLAM Algorithms in Dynamic Environments
Authors: Li Mingyang
Published: November 2024

This paper reviews the latest research progress of visual SLAM in dynamic scenes. The improved algorithms dependent on deep learning and multi-sensor fusion are discussed. First of all, the challenges of traditional visual SLAM in dynamic environments are stated, especially the interference of high dynamic objects on system feature extraction, pose estimation and mapping. Then, the application of several object detection technologies such as YOLOv5 and YOLOv7 in dynamic visual SLAM was analyzed in detail. These methods significantly improve the positioning precision and robustness of the system by identifying and eliminating dynamic feature points in the scene. In addition, this paper explores the visual SLAM system combining semantic segmentation and geometric constraint optimization, and finally introduces the multi-sensor SLAM method combining IMU data, point cloud data and visual information. Experimental results show that these improved algorithms have a significant improvement in positioning accuracy and system robustness compared with traditional visual SLAM algorithms under TUM and other dynamic data sets.
Multi-Sensor Information Fusion Technology in Robots
Authors: Feng Yiyang
Published: November 2024

Multi-sensor information fusion is a technology that collects and processes data from multiple sensors to obtain more comprehensive and accurate information. In the field of robotics, multi-sensor information fusion technology is frequently applied to robots since the use of a single sensor in robots is often limited, this will also improve the accuracy and robustness of robot environment perception. After researching and organizing multi-sensor information fusion technology, robotics technology and other related materials, this paper provides a comprehensive introduction to the application of multi-sensor information fusion technology in the field of robotics, describes the logical structure of multi-sensor information fusion technology in robotics, explains the algorithms of multi-sensor information fusion technology that are mainly applied to robots. Finally, this paper summarizes and exemplifies the main applications of multi-sensor information fusion technology in the field of robotics.
Application of Different Sensors in Obstacle Avoidance of Delivery Robots
Authors: Yu Huanchen
Published: November 2024

People's living conditions are constantly rising as the global economy and society evolve at a rapid pace, and improving production efficiency has become a crucial concern. This paper proposes a combination of multiple obstacle avoidance schemes and algorithms to achieve dynamic obstacle avoidance for delivery robots. By analysing three different obstacle avoidance schemes including visual obstacle avoidance scheme, ultrasonic obstacle avoidance scheme and laser obstacle avoidance scheme, three robot usage scenarios including hotels, restaurants and hospitals, and three vital algorithms including Dijkstras algorithm, ORCA algorithm and PID algorithm. Then by analyzing the advantages and disadvantages of each scheme and algorithm and the characteristics of different usage scenarios, this paper obtains a robot obstacle avoidance and algorithm usage plan in a specific scenario.
Enhancing Industrial Automation Through AI-driven Sensors: A Comprehensive Study on Efficiency, Safety, and Predictive Maintenance
Authors: Qi Mengshi
Published: November 2024

The fast development of the industry of Industry 4.0 is reshaping the manufacturing landscape. The key to this transformation is the integration of sensors with artificial intelligence, making traditional sensors capable of processing data and making decisions. This paper will concentrate on how AI-driven smart sensors-such as temperature, pressure, infrared, and optical sensors are redefining industrial automation. Followed by case studies across sectors like manufacturing and energy demonstrate how sensors reduce downtime, optimize performance, and anticipate failures.
Overview of Sensor Applications in Intelligent Driving
Authors: Yan Shuaikun
Published: November 2024

With the rapid development of autonomous driving technology, sensors are the core component of the intelligent driving system, whose selection and combination are crucial to the safety and efficiency of the system. This paper reviews the applications of different types of sensors in intelligent driving systems, including cameras, LIDAR, millimeter-wave radar, and infrared sensors. By analyzing the best choice of these sensors in urban traffic, highway, night driving and field driving, this paper explores the effectiveness of the sensor combination and cites the relevant literature in the past five years.
A Review of Multi-Sensor Fusion Techniques for Indoor Mobile Robot Navigation
Authors: Shan Tianyi
Published: November 2024

The application of multi-sensor fusion technology in indoor mobile robot navigation and localization has been increasingly gaining attention, especially in complex indoor environments where achieving high-precision autonomous navigation poses a significant challenge. This review summarizes various sensor fusion methods, including the combination of Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU), Ultra-Wideband (UWB), and others, with a focus on discussing the progress of fusion algorithms such as Extended Kalman Filter (EKF) and Adaptive Monte Carlo Localization (AMCL) in improving navigation accuracy and stability. In addition, this paper explores the application of visual Simultaneous Localization and Mapping (SLAM) methods incorporating deep learning in indoor robot navigation. Finally, the main challenges currently faced by multi-sensor fusion technology in robot autonomous navigation are analyzed, and future research directions are proposed, aiming to provide valuable references for researchers in the field.
A Review of Gait Coordination Control Methods for Lower Limb Exoskeleton Robots Based on Visual Models
Authors: Yang Lihao
Published: November 2024

The ongoing evolution of exoskeleton technology has rendered the exclusive reliance on a single vision technology an impractical approach to addressing the complexities of diverse environments and individual requirements. This paper therefore explores the potential of combining traditional visual models with movement intention prediction based on surface electromyography (sEMG) and virtual reality (VR) technology in the context of gait control. The study concluded that traditional visual models are capable of providing accurate environmental perception and gait planning; however, they are deficient in forecasting user intentions. Surface electromyography (sEMG) technology can supplement this deficiency with muscle activity signals, enabling the system to respond dynamically to user needs. Concurrently, virtual reality (VR) technology furnishes users with a more personalized training program through immersive environmental simulation and feedback. The combination of these three technologies not only enhances the system's real-time responsiveness but also exhibits commendable adaptability in complex terrain and rehabilitation training. This paper examines the three fundamental technologies of lower limb exoskeleton robots in gait control and their fusion applications, with the objective of enhancing the system's intelligence and adaptability, providing a direction for the future development of lower limb exoskeleton technology, and highlighting the challenges and potential of multimodal data fusion and computational optimisation.
Advanced Reversible Data Hiding Techniques in JPEG Images: Methods, Applications, and Future Perspectives
Authors: Li Xinyi
Published: November 2024

This paper provides an extensive review of recent advancements in reversible information hiding methods tailored for JPEG images, a predominant compression standard crucial for data security and digital copyright protection. It starts by outlining fundamental concepts related to JPEG image structure and reversible data hiding techniques. The analysis then delves into three primary methods: modifications to JPEG quantization tables, adjustments in Huffman tables, and corrections of Discrete Cosine Transform (DCT) coefficients, detailing how each technique contributes to effective and secure data embedding. Furthermore, the exploration of potential applications in critical areas highlights the versatile utility of these techniques in safeguarding digital content. Concluding with a discussion on future research directions, this paper emphasizes the significant potential for growth in this field, particularly in enhancing security measures and improving efficiency. Such advancements promise to extend the applicability of reversible data hiding, ensuring robust copyright protection and information security in the digital age.
Enhancing Greenhouse Efficiency: A Quantum Genetic Algorithm Approach to Optimal Glass Thickness
Authors: Li Xinkai
Published: November 2024

Greenhouses are pivotal in extending the growing season for various crops by creating conducive environments that enhance plant growth. A critical factor in maximizing the efficiency of greenhouses is the optimization of light transmittance through multi-layer glass structures. This not only supports plant health but also conserves energy by maintaining a warmer internal climate. The application of quantum genetic algorithms (QGAs) to this context represents a significant advancement, as these algorithms excel in handling multi-objective optimization challenges. By targeting the precise calibration of glass thickness, QGAs can substantially influence the amount of solar energy harnessed within the greenhouse, especially when the sun is directly overhead at noon. This paper presents an in-depth study on the use of quantum genetic algorithms to optimize the thickness of three distinct layers of glass in greenhouse settings. Our findings reveal that QGAs are capable of generating various combinations of glass thickness that not only maximize the total energy transmitted but also ensure stability in the energy output across different scenarios. The robustness of QGAs in consistently deriving optimal solutions underscores their potential as a superior technique in architectural design for agricultural applications. Comparative analysis with classical genetic algorithms and random number optimization techniques further demonstrates the superiority of quantum genetic algorithms.
Forecasting Grain Production in Hubei Province Based on Markov Modeling
Authors: Yang Jinpeng
Published: November 2024

Abstract. Grain production plays a crucial role in economic and social development. Accurate prediction of grain production can help agricultural decision-makers allocate resources rationally and do a good job at the next stage of the arrangements for the deployment of the work.This article used the production data, which was from 2023 Hubei Provincial Statistical Yearbook,to simulate and predict the grain production for several years,with the method of Markov prediction. It can be seen from the results that the average fitting error of the model is 3.47%. Using the text dataset of grain production from 2020 to 2022, the results show that the model can effectively improve the prediction accuracy of agricultural grain procuction. Finally, the model is used to predict the agricultural output value structure of China from 2023 to 2027. Markov prediction can improve the predictability and initiative of the work and provide scientific basis for the future decision-making of agricultural managers, and has guiding significance for the work of grain storage and distribution.
User Behavior and Satisfaction in AI-Generated Video Tools: Insights from Surveys and Online Comments
Authors: Hu Yurui
Published: November 2024

Abstract. With the rapid advancement of AI, AI-generated video tools have become essential in digital content creation. However, there are significant differences in user acceptance and satisfaction with these tools. This study aims to explore the underlying reasons for these differences by analyzing user behavior, satisfaction levels, and preference characteristics through a questionnaire survey and online review data. Ridge regression analysis identified usage frequency and data security concerns as key factors influencing satisfaction, with familiarity and education level also playing significant roles. Based on these factors, K-means clustering categorized users into three groups: occasional users with high satisfaction, functionality-focused users with the highest satisfaction, and frequent users with lower satisfaction due to data security concerns. Sentiment analysis and LDA topic modeling of online reviews for five AI video tools-Animoto, Invideo, Lumen5, Pictory, and Synthesia-revealed differences in user evaluations. The findings indicate that Lumen5 excels in social media content creation, while Pictory has a weaker user experience with more negative feedback. The study concludes that developers should focus on increasing tool usage frequency and enhancing data security to improve user satisfaction. Moreover, targeted strategies based on different user segments can aid in more precise product optimization and market promotion. This research provides practical guidance for the further development of AI-generated video tools. Future studies could expand the sample size and data dimensions to further validate and deepen these conclusions.
Research on National Short-Term Precipitation Forecast Method Based on Machine Learning
Authors: Wang YiTing
Published: November 2024

Abstract. Climate change is closely related to human lives. With the development of information technology, meteorological data gradually show the characteristics of big data. The purpose of this study is to compare the application effects of different machine learning models in short-term precipitation prediction, to improve the accuracy and efficiency of prediction. Based on the annual average precipitation data of prefecture-level cities in China from 1990 to 2022, this paper uses Random Forest, eXtreme Gradient Boosting and Neural Networks to construct prediction models, and comprehensively evaluate them. In this study, the data is preprocessed to ensure the data quality of the input model. Next, the data is fed into three machine learning algorithm models, Random Forest, eXtreme Gradient Boosting and Neural Networks. Finally, the prediction performance of each model is evaluated by various indexes. In this paper, it is found that the Random Forest model has the best performance, and its prediction accuracy is higher than the other two models, which has great application potential in the field of short-term precipitation prediction. This study shows that a reasonable selection of machine learning methods and optimization of model parameters can effectively improve the accuracy of short-term precipitation prediction. This paper provides some empirical evidence for precipitation prediction, which will help to make more effective decisions in dealing with extreme weather events and climate change challenges in the future.
Analysis and Prediction of Risk Factors for Heart Failure
Authors: Wan Yu , Zhang Shuya , Zhao Yuxuan
Published: November 2024

Abstract. Heart failure is a grave and progressive illness. It is associated with multiple risk factors such as age and other possible factors. Accurately identifying and quantifying these risk factors is critical to developing personalized prevention and treatment strategies. However, it is difficult for common patients to predict heart failure without the professional diagnosis of doctors, and relying on human resources to predict heart failure is more subjective. This study proposes a method to evaluate and predict the factors related to heart failure based on multiple body indicators by using random forest algorithm. The random forest prediction model was applied to assess the correlation between multiple medical indicators such as creatinine phosphokinase (CPK), serum creatinine (SCR), ejection fraction (EF), age and heart failure. CPK was found to be the most associated with heart failure. In addition, increasing follow-up period can also effectively monitor heart failure progression in patients. In this high dimension prediction problem, the prediction effect of random forest is better, and the overall accuracy is higher. The approach used in this research is important for forecasting the risk of heart failure, enhancing the survival rates of patients, and alleviating the burden on healthcare systems.
The Impact of Movie Text Information on Movie Recommendation Systems based on Multi-Armed Bandit
Authors: Guo Yitong
Published: November 2024

Abstract. The multi-armed bandit problem has been extensively studied and applied in various fields such as recommendation systems and resource allocation. However, there is still a lack of research in the movie recommendation domain, such as the comparison of algorithm performance and the impact of movie text information. This paper uses the Movielens 100K movie review data set to compare and analyze the performance of classic algorithms-Greedy, UCB, and TS-in the multi-armed bandit problem. Additionally, to explore the influence of movie text information (such as movie summaries and user reviews) on system algorithms, this experiment utilizes web scraping technology through HTTP requests and HTML parsing, to extract data from the Douban website, obtaining the information and forming a new data set. In this movie review data set, the Greedy algorithm outperforms both UCB and TS algorithms. This study finds that incorporating movie text information improves the accuracy of all three algorithms, thus contributing to the enhancement of movie recommendation systems. This experiment did not consider larger datasets or movie image information, which could be addressed in future improvements.
Research on Urban Traffic Flows Prediction Model Based on Deep Learning
Authors: Dai Xiangcheng
Published: November 2024

Abstract. Vehicle accumulation in urban regions is a significant complication, giving rise to monetary and sustainability-related concerns. The most important procedure for effective traffic management is the precise forecasting of traffic flow. This study leverages Long Short-Term Memory (LSTM) networks to predict traffic volume on Interstate-94 (I-94) in the US, using hourly traffic and weather data from 2012 to 2018, which was normalized using Min-Max scaling, and the LSTM model was trained on 80% of the data, with the remaining 20% used for testing. Evaluation of the model was conducted using performance indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Showing its strong forecasting ability, evidenced by its low MAE and RMSE, which highlight the model's high accuracy, and forecast the traffic flow under varying conditions. This study highlights the effectiveness of LSTM networks for traffic prediction, offering a significant tool for the management of the metropolitan areas. Upcoming studies might prioritize on real-time implementation and integrating additional data sources to further enhance prediction accuracy.
The Investigation of Progress and Application in the Multi-Armed Bandit Algorithm
Authors: Feng Yixuan , Liu Shuaiyan , Wang Jiashuo
Published: November 2024

This article delves deeply into the development process and practical applications of the multi-armed bandit algorithm in the current digital era. With the continuous popularity of online advertising and online learning, information has grown explosively, making decision optimization crucial. The multi-armed bandit algorithm, as a sequential decision-making model, encompasses common algorithms such as the greedy algorithm, -greedy algorithm, UCB algorithm, and Thompson sampling. Its main role is to seek the best balance between exploration and exploitation to solve the fundamental problems in reinforcement learning. The article introduces an internationally released datasets, namely MovieLens, and elaborates in detail a series of indicators for evaluation, including the average number of friends per user, the average number of listened-to artists per user, the average number of movie rating times, the average number of tags added by users, content diversity indicators, and statistics on the differences in click-through rates of recommendations for different types of movies. In addition, the article also presents the specific methods of literature collection, screening, analysis, and review. Its purpose is to understand the multi-armed bandit algorithm more deeply and provide strong guidance for the future development and wide application of this algorithm in various fields.
Application of Deep Learning in Sentiment Analysis Based on Movie Review Datasets
Authors: Yang Ziyi
Published: November 2024

Abstract. With the rapid development of the Internet and social networks. user-generated content (UGC) has exploded on the Internet as an important form of user feedback, especially movie reviews, which are of great value to the film industry and market analysis, and can help relevant groups understand the reputation and evaluation of movies. As a natural language processing technology, sentiment analysis can effectively identify and extract emotional biases in text, providing a new perspective and method for sentiment analysis from film criticism. The results show that using deep learning can significantly speed up this work and make sentiment analysis more efficient and faster. The purpose of this article is to explore the application and specific process of deep learning in sentiment analysis of film criticism datasets, to find the most appropriate model by comparing and analyzing multiple models, and to present the rating data of some films from the perspective of display technology.
Enhancing Multilayer Glass Transmittance through Particle Swarm Optimization: An Experimental Approach
Authors: Xiao Hongyu
Published: November 2024

Abstract. This study focuses on enhancing the solar transmittance of multilayer glass in buildings located in cold northern regions to improve natural lighting efficiency and maintain indoor warmth in harsh climates. The Particle Swarm Optimization (PSO) algorithm is utilized to optimize the glass layer thickness, and an optical model is constructed to calculate the light transmittance of the multilayer glass. Through simulations and experimental analysis, the impact of different glass thickness combinations on overall transmittance is investigated. The study examines the optical properties of gradient glass and calculates changes in light transmittance under varying thickness combinations using MATLAB software. The PSO algorithm is applied for global optimization to identify the glass thickness combination that maximizes light transmittance within the specific visible light range (400nm-800nm). The experimental results, including spectral energy distribution before and after sunlight transmission, validate the accuracy of the theoretical model. The findings demonstrate that the optimized glass thickness combination significantly improves solar transmittance, especially within the visible light band, contributing to more energy-efficient and comfortable building environments in cold regions. Additionally, the potential of the PSO algorithm in global search and local convergence is analyzed, suggesting future research directions to further enhance optimization through dynamic adjustments or combining with other algorithms.
A Comparative Study of Swin Transformer and CNN Models for Crowd Counting
Authors: Guo Jiayi
Published: November 2024

Abstract. Crowd counting, a critical component in the management and safety planning of large gatherings and public spaces, is essential for ensuring smooth event operations and preventing potential overcrowding issues. While the standard convolutional neural network (CNN) based model performs well in head counting tasks, it has certain drawbacks when applied to complex scenarios. With the rapid development of artificial intelligence, Transformer models that rely on self-attention mechanisms, as Swin Transformer, have demonstrated exceptional performance in visual tasks, such as image classification, and segmentation in recent times. This study examines the experimental findings of Swin Transformer's head counting tasks and contrasts them with the CNN-based model. Mean Absolute Error (MAE) and Mean Square Error (MSE) evaluation indicators show that the Transformer model outperforms the classic CNN model in terms of generalization abilities when dealing with complicated scenarios. Future research work will increase the diversity of data sets and focus on optimizing model structure and improving training efficiency.
AI-Assisted News Content Creation: Enhancing Journalistic Efficiency and Content Quality Through Automated Summarization and Headline Generation
Authors: Yuan Fanbu , Shi Ruonan , Li Ziwei
Published: November 2024

Abstract. Nowadays, Artificial Intelligence (AI) is a buzzword which appears on tech headlines and guides constantly. In a nutshell, AI has been widely implemented in news content generation process, which helped journalists to develop more efficient, diverse and qualitative news production workflows. In this paper, we discuss the applications of AI in automated summarisation, headline generation and content optimisation in newsroom-wide workflow. We analyse this process by looking at how AI tools could potentially reduce human efforts and help humans to produce more concise and accurate summaries and fascinating headlines by also enhancing the engagement of the audience. However, we also argue about the increasing dependency on AI which potentially causes more ethical problems such as algorithmic bias, lack of transparency, and the loss of journalistic integrity. By examining the current AI-driven tools and strategies to avoid the bias produced by machines, we explore both the opportunities and the challenges of AI in journalism. Our findings imply that, although AI substantially improved the production efficiency and content relevance, it requires proper management to prevent the problem such as ignorance of bias and ethical failure. Eventually, humans would still play a part in the story by complementing AIs capabilities.
Exploring RSA Cryptography: Principles and Applications in Image Encryption and Microcontroller Security
Authors: Ma Chen
Published: November 2024

Diffusion models in AI-generated content (AIGC) have revolutionized image generation with their ability to transform noise into structured data through a denoising process. Despite their prowess, these models grapple with high computational demands, inconsistent image quality, and challenges in producing high-resolution images. This paper discusses the performance of diffusion models in image generation, emphasizing the need for improved computational efficiency, generation quality, and contextual understanding. Future research is aimed at developing real-time generation technologies, addressing ethical and legal concerns, and enhancing data privacy measures. With these advancements, diffusion models are expected to achieve broader application, enhanced performance, and increased safety in image generation, expanding their utility in fields such as advertising, medical imaging, and art creation. The potential of diffusion models to meet the increasing demands for quality, content, and diversity in image applications makes them a pivotal area of study in visual technology and artificial intelligence.
A Comprehensive Review of Diffusion Models in AI-Generated Content for Image Applications
Authors: Jia Yuxin
Published: November 2024

Diffusion models in AI-generated content (AIGC) have revolutionized image generation with their ability to transform noise into structured data through a denoising process. Despite their prowess, these models grapple with high computational demands, inconsistent image quality, and challenges in producing high-resolution images. This paper discusses the performance of diffusion models in image generation, emphasizing the need for improved computational efficiency, generation quality, and contextual understanding. Future research is aimed at developing real-time generation technologies, addressing ethical and legal concerns, and enhancing data privacy measures. With these advancements, diffusion models are expected to achieve broader application, enhanced performance, and increased safety in image generation, expanding their utility in fields such as advertising, medical imaging, and art creation. The potential of diffusion models to meet the increasing demands for quality, content, and diversity in image applications makes them a pivotal area of study in visual technology and artificial intelligence.
Enhancing Object Sorting Under Low-Light Conditions with CLAHE, Gaussian Blur, ROI, and Custom PID on a Raspberry Pi Robotic Arm
Authors: Wu Jie
Published: November 2024

Abstract. This paper addresses the significant challenge faced by robotic vision systems in detecting and sorting objects accurately under varying lighting conditions. Such variations in light can lead to decreased detection accuracy and inefficiencies in automated sorting processes. The paper employs a combination of literature review and experimental validation to investigate the effectiveness of advanced image processing techniques and control algorithms. Specifically, it explores the application of CLAHE adaptive compensation, Gaussian Blur, custom ROI, and PID controllers within a visual object sorting system to improve its robustness under diverse lighting conditions. The use of CLAHE and Gaussian Blur effectively compensates for uneven lighting, while custom ROI and PID controllers further optimize the system's response to fluctuating conditions.
A Review on the Application of Automatic Text Summarization and Its Bias Analysis
Authors: Li Zehao
Published: November 2024

Abstract. In the era of information explosion, the Internet produces a large number of text documents daily, providing rich information but also posing a challenge: how to swiftly extract key information. Traditional manual reading is time-consuming and inadequate for handling vast data. Thus, automatic text summarization technology emerges as a crucial solution. This paper reviews the application and deviation analysis of this technology across various fields, focusing on addressing shortcomings of traditional methods, such as initial cluster center selection and redundancy. An automatic text summarization method based on an improved TextRank algorithm and K-Means clustering is introduced. Existing methods often struggle with inaccurate initial clustering center selection and high summary redundancy, especially with long texts, resulting in summaries that fail to reflect core content accurately. Furthermore, the widespread use of pre-trained language models introduces potential biases that can propagate to downstream tasks, affecting summary accuracy and impartiality. To address these issues, this paper proposes an innovative automatic text summarization method that optimizes initial clustering center selection and clustering refinement strategies to enhance summary accuracy and readability. Additionally, it discusses name-nationality bias in pre-trained language models and its propagation in text summary tasks, offering a theoretical foundation and practical guidance for developing a more just and reliable Natural Language Processing (NLP) system.