bluuebunny/crossref_metadata_2025 huggingface dataset:
Analyzing the Circumstances under Which Art Was Born from Italian Neorealism - The Suffering of the Times and the Rise of Art
Authors: Wang Yuang
Published: November 2024

Abstract: This essay is a unified study of the formation and development of Italian Neorealist cinema in Italy from the pre-World War I period to the post-World War II period as the time of study. Taking Italian Neorealism as an example, it explores the relationship between the suffering of the times and the rise of art. In addition to how Italian Neorealism was born against the backdrop of war and social upheaval, it influenced the subsequent French New Wave cinema and global cinema, and in particular generated far-reaching references in Third World cinema. By analyzing the gradual decline of Italian cinema from its peak in the early twentieth century, which relied on its historical and cultural heritage, through the impact of Hollywood globalization and the economic hardships of World War I, all the way to the suffering of Italian society and the collapse of the economic market during and after World War II, the study demonstrates how Italian Neorealism, through its use of long shot filming, non-professional actors, live-action productions, and documentary narratives, redefined the mission of cinematic art beyond its traditional entertainment function to give voice to the underclass. The article delves further into the influence of Italian Neorealism on the French New Wave and Third World cinema, particularly regarding its influence on social realism and political critique. It is expected to continue inspiring future filmmakers, especially those working in independent and developing country cinema.
L2 English Learners Perception of English Diphthong /u/ and Chinese Pinyin (wo) /uo/ (nest) in Phonetic Contexts
Authors: Wen Wanyi
Published: November 2024

Abstract: In English phonetics instruction, vowels often present challenges for second language learners. The articulation of vowel is complex and can be difficult for learners to be accurately grasped, making it hard for them to perceive the precise where English vowels are produced. Additionally, the vowel systems of English and Chinese differ significantly. Native Chinese speakers have internalized the Chinese vowel system, leading to pronunciation patterns that do not easily align with those of English. This paper examines the differences between the vowel systems of the two languages and the extent to which second language learners perceive specific vowels through a literature review. The findings suggest that the collected research on English and Chinese vowel system differences will serve as a valuable foundation for the upcoming experiment on the comparison and perception of the diphthongs /u/ and /uo/. Furthermore, the review finds that conducting experiments on the perception of diphthongs with native Chinese speakers can provide valuable insights for further development in bilingual classrooms of second language learning.
From the Village to the City: The Rise of Urban Folklore Studies in China
Authors: Chen Keji
Published: November 2024

Abstract: The rise of Chinese folklore study was an academic response to the Modern Chinese Enlightenment Movement. The shift in perspective of Chinese folklore studies from the rural field to urban society is the symbol self-reflective turns and innovations. The shift from rural to urban folklore requires an adjustment of research perspectives and methods, not only to adapt to the urbanized social environment, but also to cope with the more complex modern society. Cities are fast-paced, mobile societies, where peoples general and instinctive rejection of strangers and the highly compressed and fragmented nature of their leisure time make fieldwork more difficult. Human behavior is not homogeneous, but diverse, the study of urban folklore can draw on personal life history methods to take a microscopic look at urban society. The rise of Chinese urban folklore is a new area of Chinese folklore studies, and its research theory, methodology and subjects still need to be further explored and supplemented by scholars.
Analysis on the Enlightenment Effect of Sesame Street on Children's Personality Exploration and Self-identity
Authors: Wang Mengyuan
Published: November 2024

Abstract: In recent years, with the progress of social economy and the development of education, children's mental health has increasingly become the focus of attention. As an important medium of children's education, TV programs are very popular with children, and have a profound significance in the shaping of children's health. American television program Sesame Street as a popular children's program, from the plot content, role setting, theme selection, present form, etc., conforms to the preschool childrens acceptance preferences, and successfully utilizes the medium for the key period of childrens mental development, accurately grasps the mental health characteristics of preschool childrens love of imitation. The program plays a good enlightening role in guiding children to discover their own personality and construct the self identity. The concept of "personality exploration and self-identity" is related to "self". In the pursuit of personality and self-positioning, children are guided to establish self-awareness, cultivate self-management ability and self-respect feelings. This paper mainly analyzes the enlightenment effect of Sesame Street on children's personality exploration and self-identity. The program promotes children to establish confidence, form self-identity, constitute the supporting conditions of mental health, for children to lay a solid foundation, and promote the formation of children's mental health and sound personality. At the same time, as a successful TV program that is deeply loved by children and has the significance of health education, it provides examples and references for creating more high-quality children's TV programs.
The Application and Implications of Memetics Theory in Second Language Acquisition
Authors: Zhang Chi
Published: November 2024

Abstract: In recent years, with the continuous development of language learning theories, the traditional model of second language acquisition has gradually exposed its limitations and is unable to fully explain the complex language learning process. Meanwhile, Memetic Theory, as a theory that explains cultural transmission and imitation, has shown potential application in the field of language learning. As a result, this paper explores its application strategies and implications in second language learning. Specifically, through a literature review of traditional language learning theories (e.g., behaviorism, cognitivism, and social constructivism), it analyzes the shortcomings of these theories in language learning learning, and proposes modal theory as an alternative theory. In addition, it examines the impact of modality on the vocabulary retention, grammatical proficiency, and phonological imitation of language learners, and assesses the potential benefits of modal propagation in language teaching and learning by integrating it with actual teaching cases. In short, the study, which employs a systematic application of modality theory to the field of language learning, can provide new perspectives and methods for second language acquisition, thereby enhancing the efficacy of language learning.
Understanding the Patient's Rigid Patterns in Psychotherapy From the Perspective of Compensation under Classical Conditioning
Authors: Liu Xinnan
Published: November 2024

It is common in psychotherapy to get bogged down in treatment progress because of patients' rigid patterns, and the first step to better dealing with this problem is to understand rigid patterns better. Therefore, it is essential to start with the concept of compensation under classical conditioning to help understand the rigid pattern of patients in psychotherapy. Given that the understanding from this perspective is relatively new and largely fragmented, it is theoretically important to evaluate what is being studied and gain meaningful insights through a structural review of the literature. After reviewing the extensive literature, this study also made some feasible suggestions on how to better understand and deal with patients' rigid patterns to assist therapists in dealing with such issues, combining the approaches taken by different schools of psychotherapy. This article is believed can provide a useful basis of understanding for future psychotherapists to better deal with patients' rigid patterns in practice.
Image splicing detection using integrated LBP and DCT features
Authors: Su Hang
Published: November 2024

Abstract. Image splicing is one of the most common techniques used for picture manipulation and forgery. With the advent of user-friendly photo editing software, image splicing has become more prevalent and increasingly difficult to detect. This paper proposes a passive photo splicing detection approach based on Local Binary Patterns (LBP) and Discrete Cosine Transform (DCT) to identify splicing forgeries. The input RGB images are first converted to the YCbCr color space. Subsequently, the chrominance channels, Cb and Cr, are divided into overlapping blocks. Each block's LBP code is then transformed into the DCT domain. For each block, the standard deviation of each DCT coefficient is computed and used as a feature. Support Vector Machine (SVM) is employed as the classifier in a predictive model to determine whether the images have been spliced. To evaluate the proposed approach, two benchmark datasets for photo tampering were utilized. Experimental results indicate that the proposed method outperforms traditional splicing detection techniques in terms of detection accuracy and performance. This enhanced detection capability underscores the potential of combining LBP and DCT features with SVM classification for robust image splicing detection, paving the way for improved digital forensics tools in combating image manipulation.
Dockless shared bicycles accumulation point analysis based on DBSCAN
Authors: Leng Chuchu , Yin Jinjia
Published: November 2024

Abstract. As bike sharing becomes more and more popular, how to optimize the use experiences is now turning into a problem. In many cities, how to allocate the dockless shared bicycles delivery is important for providing convenience for citizens. Based on sufficient data which is originated from a shared bicycle company called Mobike in Shanghai, this paper used Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to analysis the major accumulation points to help rebalance the allocation. In order to intuitively feel the concentration level, this study divided the points into 3 levels: the most accumulated, the more and the relatively least, which was generally corresponded with the previous study. The most accumulated points were concentrated on Yangpu District, followed by some points in Jing'an and Minhang District. The outer ring of city was relatively less accumulated, but also more accumulated than other areas. The 3 levels significantly differentiated the data, making convenience for staffs to optimize the allocation.
Research on positive and negative sample sampling strategies in contrastive learning
Authors: Liu Wenyi
Published: November 2024

Abstract. Contrastive learning is an important technique in the field of machine learning for learning data representations. In the field of self-supervised visual representation learning, the strategy of positive and negative sample selection is key to improving the efficiency and effectiveness of model learning. Traditional self-supervised learning methods often employ random sampling strategies to select positive and negative samples, but this approach may result in uneven sample quality when dealing with complex datasets, thereby affecting the learning outcomes. To alleviate this problem, this study is dedicated to exploring more effective strategies for positive and negative sample selection and processing to optimize the self-supervised learning process. To this end, we propose an improved method of self-supervised learning called contrastive learning with enhanced diversity. On the one hand, this method utilizes the weight parameters of the DINO pre-trained model to initialize the feature extraction network of SimCLR, providing more accurate calculations of feature similarity. On the other hand, by setting a threshold on the feature similarity matrix and penalizing (subtracting 0.5 from) similarity scores that do not exceed this threshold, we reduce the excessive impact of high similarity scores on model training, thereby helping the model better distinguish between positive and negative samples. In downstream image classification tasks, we conducted detailed evaluations of the improved model, specifically including fine-tuning and linear evaluation aspects. Experiments show that the proposed approach improves the performance of the loss functions and improves the accuracy of the proposed SimCLR model.
Enhancing periodic mobility planning: ARIMA-driven prognostications for subway passenger volume dynamics
Authors: Zhang Minxuan
Published: November 2024

Abstract. This study aims to accurately predict urban subway passenger flow using the Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto Regressive Integrated Moving Average (SARIMA) model, with the intention of providing a scientific basis for subway operation management. By meticulously sorting and analyzing the existing traffic data of a large city's subway system, this research constructs a model that comprehensively considering the seasonal, trend, and stochastic characteristics of time series data. During the model construction process, data preprocessing is carried out, including stationarity tests, differencing, model order determination, and parameter estimation, ultimately identifying an ARIMA and SARIMA model with good fitting results, effectively identifying and forecasting characteristics such as numerical fluctuations. Additionally, this study conducts a special analysis of the prediction effects at diverse time scales, verifying the applicability and superiority of the ARIMA and SARIMA model in subway passenger flow prediction. This research not only provides a powerful tool for passenger flow prediction for subway operation management departments, aiding in the reasonable allocation of capacity and optimization of operation strategies, but also holds significant reference and application value for the passenger flow prediction and management of other urban public transportation systems.
Quantum mechanics and statistical physics: Novel frameworks for enhancing natural language processing
Authors: Wan Meiyan
Published: November 2024

Abstract. This article explores the pioneering application of principles from quantum mechanics and statistical mechanics to the field of natural language processing (NLP). By drawing analogies between physical phenomena such as quantum entanglement, phase transitions, and statistical ensembles, and linguistic concepts like semantic relationships, language use dynamics, and lexical diversity, we offer a novel perspective on language analysis and processing. Quantum linguistic models, leveraging the intricacies of entanglement and quantum probability, provide a framework for understanding complex semantic networks and enhancing computational efficiency through quantum computing. Meanwhile, statistical mechanics inspires models for capturing lexical diversity and understanding the evolution of language patterns, akin to phase transitions in physical systems. This interdisciplinary approach not only deepens our understanding of linguistic phenomena but also introduces advanced mathematical and computational techniques to improve NLP tasks.
Navigating the intersection of computer technology and IoT: Innovations, challenges, and strategies for privacy protection
Authors: Li Zesheng
Published: November 2024

Abstract. The integration of computer technology in the Internet of Things (IoT) has introduced new efficiencies, and conveniences in many sectors. However, this integration has also brought some serious challenges, and one of the most pressing ones is the need for more effective protection of privacy. This paper investigates some of the latest security developments in IoT industry, including encryption, blockchain, and artificial intelligence. Furthermore, it highlights the important challenges in IoT technology by providing an overview of data overload, regulatory and security issues in IoT devices. Besides, possible solutions to these problems are suggested in this paper. In addition, a detailed conclusion is made, which includes three future plans and some important recommendations to researchers, practitioners, and policymakers who are interested in achieving effective IoT security and privacy. The paper will have a positive impact on the research community, as it will highlight the growing need to invest in research and collaboration to improve the security of the IoT ecosystems.
Integrating computer vision and AI for interactive augmented reality experiences in new media
Authors: Shi Ruonan
Published: November 2024

Abstract. Augmented reality (AR) is a groundbreaking technology that fully immerse the user in a mixed 'reality' where the real and virtual coexist in a unique manner. Integrating more artificial intelligence (AI) and computer vision into AR devices can greatly improve user input and provide a whole novel interface. Through AI technology, such as gesture recognition, object tracking, and face recognition, AR systems can offer more intuitive and engaging interactions. An AR system featuring such improved AI technologies can process real-time data while having the contextual awareness to respond to users' input and the surrounding environment on the fly. It can also provide narrative integration, character development, and dynamic environments to users, thereby enabling them to have a more personalised and meaningful experience. This paper examines the evolution of new media by discussing the possibilities of using AI and computer vision in AR devices to create personalised experiences, all the while critically looking at technical challenges and the opportunities they present to the field of future research and development. It also looks at case studies across different sectors, such as education, training, tourism, gaming, retail, and aviation to justify the potential of future development of AI-enhanced AR.
Application and optimisation of intelligent control system for home robots
Authors: Ding Rui
Published: November 2024

Abstract. As a significant part of the smart home, intelligent home robots can provide a variety of convenient home services such as automatic cleaning, elderly care, entertainment interaction, etc., which are highly valued and favoured by the majority of home users, and the market scale is expanding. However, the design and optimization of the control system represents a crucial technical challenge to truly realize the intelligence and autonomy of home robots. This paper presents a comprehensive analysis of the design and optimization of automatic control systems for intelligent home robots, which aims to improve the performance and user experience of intelligent home robots and better meet the needs of families. Through collection and analysis of the research on domestic robots and their control technology ,the core technical elements of the control system are summarized and refined, and the hardware structure and software framework of the control system are analyzed. The research results provide valuable insights for improving the performance and capabilities of home robot control systems to better meet the diverse needs and expectations of home users. These innovations provide a solid foundation for advancing the development of intelligent robots in the home. Future trends in control systems may include enhanced perceptual capabilities and decision-making intelligence to enable robots to better understand and meet the needs of home users.
Research on autonomous driving technology in intelligent transportation systems
Authors: Zheng Shuhan
Published: November 2024

Abstract. The convergence of scientific and technological advancement with the acceleration of urbanization has resulted in a notable intensification of the issue of traffic congestion. In this context, the development of autonomous driving technology has emerged as a pivotal area of focus for future transportation systems. Therefore, this paper presents a review of the current status of automatic driving technology in intelligent transportation systems, based on methods such as literature, data analysis and case studies. Furthermore, it delves into the evolution, applications, challenges and solutions of automated driving technologies in intelligent transport systems, and presents forward-thinking insights into the future development of intelligent transportation. As autonomous driving technology continues to evolve, the integration of deep reinforcement learning and other advanced technologies will facilitate the advancement of intelligent vehicle human-machine collaborative decision-making technology. It is imperative that the construction technology of safety-critical scenarios be reinforced in order to enhance the interpretability of algorithmic models and the real-time nature of scenario generation. All parties need to collaborate to promote the innovation and improvement of autonomous driving technology, achieve widespread application and sustainable development, and create a better travelling environment.
Short-term passenger flow prediction of urban rail transit based on ARIMA model
Authors: Li Shangyun , Wang Ziyi
Published: November 2024

Abstract. This paper is about the short-term passenger flow prediction of urban rail transit based on the ARIMA model. Due to the acceleration of urbanization and the increasing density of urban rail transit, more and more people are taking urban rail transit. However, excessive short-time passenger flow can lead to traffic accidents. Therefore, it is very important to predict short-term passenger flow. This paper aims to use the ARIMA model to predict the passenger flow in a short period of time in urban rail transit. A competition data set from Alibaba about the short-time passenger flow of a metro station in Shenzhen was adopted and analyzed by the ARIMA model. The data set contains 1,428 samples, and the time granularity is 10 minutes. After research, it has been found that the ARIMA model can basically predict the short-time passenger flow of the metro with accuracy under the appropriate parameter setting. This also means that the application of the ARIMA model can be extended to other aspects of short-time passenger flow prediction, not just the metro.
Application of non-base station positioning system in rice transplanting
Authors: Zhao Yujun
Published: November 2024

Abstract. Rice transplanting machines have significantly increased the efficiency of rice planting and reduced labor costs. However, the transportation and loading of rice seedlings in the paddy fields still require a substantial amount of labor. In response to this need, implementing a fully automated rice transportation and loading system has become a demand for some enterprises, with the positioning of the rice transplanting machine being a critical subsystem to help locate the transportation destination. This paper, based on the theories of micro-controllers and multi-target positioning algorithms without base stations, utilizes literature review and theoretical analysis to design a base-station-free positioning system on a micro-controller. The system uses the Inertial Measurement Unit (IMU) module of the lower machine to collect the relative positions of the path nodes of the rice transplant machine. The upper machine's Multi-target Dead Reckoning (MDR) algorithm then determines the actual path, thereby pinpointing the specific location of the transplanter. This design will help complete the positioning of the rice transplanting machine in the paddy fields, reduce the impact of environmental factors on positioning accuracy, and lower the modification costs.
Enhancing environmental modeling and 3D point cloud construction for unmanned vehicles using laser SLAM and stereo vision with Convolutional Neural Networks
Authors: Cao Muwang , Jiang Jiao , Zhang Haoran
Published: November 2024

Abstract. This research presents an innovative SLAM algorithm that integrates Convolutional Neural Networks (CNNs) with LIDAR and stereo vision to significantly enhance the accuracy of environmental modeling and the construction of dense 3D point cloud maps in complex and dynamic surroundings. By processing pre-recorded video data and employing advanced image segmentation techniques, this study achieves a deep fusion of visual and geometric data, resulting in highly detailed and precise 3D representations of the environment. The experimental results demonstrate that this approach effectively detects and excludes dynamic objects, thereby significantly improving the overall quality, robustness, and reliability of the constructed maps. This work represents a substantial advancement in SLAM technology, particularly in its capability to model and capture intricate environmental details under varying conditions, making it a powerful tool for precise environmental mapping.
Research on lane detection algorithms based on GTNs
Authors: Wei Zhenfang
Published: November 2024

Abstract. Lane detection technology plays an important role in the lane departure warning and adaptive cruise control functions of autonomous vehicle systems. Traditional lane line detection includes experimental steps such as preyreatment, color space conversion, feature extraction, and lane line tracking, but there are still some problems such as recognition accuracy in complex environments and parameter adjustment dependency. To overcome these limitations, this study uses a novel method that can directly predict the parameters of the lane shape model, thus avoiding complex post-processing steps. This research method uses the network structure based on Graph Transformer Networks (GTNS) to capture richer structural information and contextual relationships, thereby improving the accuracy of detection. In terms of feature extraction, Graph Transformer Networks is used to accurately capture the structural information of the lane through the attention mechanism. At the same time, the results in the later stage show that our method has better advantages in accuracy and speed. In short, this optimization scheme not only improves the detection speed but also ensures a certain degree of accuracy. The method will perform better in the future after further optimization such as multi-sensor fusion and real-time optimization.
Research on the Development of LDO Chip Technology
Authors: Yu Fangyi
Published: November 2024

Abstract. Whether portable electronic devices are powered by rectified AC mains or by batteries, the power supply voltage will vary over a large range during operation. In order to ensure a stable power supply voltage, almost all electronic devices must be powered by a voltage regulator. In order to meet the requirements of precision electronic equipment, a linear regulator should be added to the input end of the power supply to ensure a constant power supply voltage and achieve active noise filtering. LDO can help achieve this function. This article first introduces the basic concepts of low-dropout linear regulators and sorts out the advantages of low-dropout linear regulators. Secondly, the parameters that need to be studied for LDO are sorted out, which are also the direction for breakthroughs in LDO research. The third part introduces some basic structures of LDO, including the design of LDO structure without external capacitors, the design of bandgap reference LDO structure, and the design of high PRSS structure. Finally, the application of LDO is roughly summarized, as well as the trends and challenges of LDO development.
Efficient Brain Cancer Classification Based on Advanced CNN and Transformer Technologies
Authors: Zhao Changhao
Published: November 2024

Abstract. This study addresses the challenge faced by primary medical staff in accurately distinguishing different types of brain cancer by comparing the training accuracy and iteration speed of various classification models. Specifically, thesis evaluated a basic Convolutional Neural Network (CNN) alongside two transfer learning models: EfficientNetV2 and Vision Transformer. The analysis is based on publicly available data from Kaggle, which provided a robust dataset for evaluation. The results indicate that both EfficientNetV2 and Vision Transformer models achieve significantly higher iteration convergence speeds and classification accuracy compared to the basic CNN model trained solely on the database. These advanced models demonstrate a capacity for efficient and rapid brain cancer differentiation, making them suitable for settings with limited computing resources. The study highlights that the transfer learning models can effectively support outpatient doctors by providing fast and accurate etiological screening without requiring extensive hardware. Overall, the findings suggest that EfficientNetV2 and Vision Transformer models offer a practical solution for improving brain cancer classification in primary healthcare environments, where rapid and precise diagnosis is crucial. This research underscores the potential of transfer learning techniques to enhance diagnostic capabilities while accommodating resource constraints.
Comparison of the Robustness of Multimodal Models and Unimodal Models under Text-based Adversarial Attacks
Authors: Liu Yizhi
Published: November 2024

Abstract. With the popularization of artificial intelligence technology, adversarial attacks have become a major challenge in the field of machine learning. This paper explores the robustness of multimodal and unimodal models under textual adversarial attacks, and probes to understand their differences and commonalities. By comparing and analyzing the performance of the CLIP multimodal model and BERT unimodal model under different textual datasets, it is pointed out that the multimodal model does not perform better than the unimodal model under unimodal adversarial attack when the multimodal fusion advantage cannot be reflected. On the contrary, the CLIP model, which is a multimodal model, exhibits larger robustness fluctuations similar to the BERT model under single-modal adversarial attacks. The advantages of multimodal models do not automatically translate into better robustness in all scenarios but need to be optimized for specific tasks and adversarial strategies, and the multimodal models do not have better accuracy than the unimodal models without task-specific pre-training. Both exhibit significant robustness fluctuations in the face of textual adversarial attacks. The research in this paper provides research value and further research directions for future studies
Review of Biomass Gasification and Pyrolysis Process Based on Long Short-Term Memory Network
Authors: Tang Zexin , Li Lingxi , Pang Cheng Heng
Published: November 2024

Abstract. With the global energy crisis become escalated day by day, finding an effective way of exploring the renewable energy has become an important direction as its widely resources, comparatively low environment impact. This paper has concluded the biomass pyrolysis and gasification process based on long- and short-term memory (LSTM) method, including the LSTM principle, current application, faced challenges and future development. LSTM as a kind of special recurrent neural network, which could effectively solve the gradient vanish and explosion problem in time-series data. Research shows that, LSTM has an excellent performance in terms of reaction condition and optimization, which could be able to capture the relationship between complex variables, the pyrolysis efficiency could be improved, and prediction accuracy could over 90%. During the data preprocessing and model training stage, it is vital to make sure the quality of data, including data normalisation, outlier handling and time series splitting, etc. Through these steps, a better prediction accuracy could be achieved by LSTM. At the same time, the real application cases of LSTM used in biomass pyrolysis process have been discussed, it is shown that the combination of LSTM and other machine learning techniques could obviously improve the system stability and product quality. In addition, this paper has also looked forward to the enhancement of model explainability and the prospect of uncertainty quantification, which is helpful to improve the LSTM application and practical value.
Enhancing investment strategies through machine learning: A comprehensive analysis across market sectors
Authors: Huang Siyu
Published: November 2024

Abstract. This study presents a detailed exploration of the application of machine learning (ML) models to optimize investment strategies across various market sectors, including equity markets, fixed income and derivatives, and the volatile cryptocurrency markets. We evaluate three primary ML models: linear regression, decision trees, and neural networks, based on their predictive accuracy, computational efficiency, and robustness to market volatility. A rigorous process involving backtesting and cross-validation assesses each model's performance. Our framework encompasses data preprocessing, feature engineering, model implementation, and a nuanced approach to risk assessment integrating Value at Risk (VaR) and the Sharpe Ratio. We demonstrate the models' effectiveness in predicting stock prices, interest rates, and cryptocurrency price movements. The application of our ML framework led to the development of dynamic portfolio optimization strategies that significantly outperform traditional methods. This study contributes to the understanding of ML's potential to revolutionize investment strategies, providing a foundation for future research and practical applications in financial markets.
A Comprehensive Comparative Study of Intuitive Physics Modeling in Machine Learning Trained with Cartoon and Realistic Data
Authors: Yang Songyu
Published: November 2024

Abstract. This study delves into the influence of training data typesspecifically cartoon versus realistic visual datasetson the development of intuitive physics modeling in machine learning. Intuitive physics, the inherent human ability to understand and predict the physical properties and dynamics of objects, presents a significant challenge for current AI systems to replicate accurately. Leveraging YOLOv5, a cutting-edge object detection model, this research systematically evaluates the cognitive understanding and performance of AI models trained on distinct types of visual data. The findings reveal that the visual complexity inherent in the training datasets plays a crucial role in shaping the model's ability to generalize and accurately perform intuitive physics tasks. Models trained on cartoon datasets exhibited different learning patterns and generalization capabilities compared to those trained on realistic data, providing valuable insights into the role of data representation in AI training. This research offers both theoretical advancements in understanding AI's cognitive limitations and practical guidance for designing AI systems that can interact with the physical world more effectively. Ultimately, the study contributes to bridging the gap between human cognition and machine learning, pushing the boundaries of what AI can achieve in modeling complex, real-world phenomena.
Advancements in Image Recognition: Comparing CNNs and Vision Transformers
Authors: Yan Mengxuan
Published: November 2024

Abstract. This paper explores advancements in image recognition technologies, highlighting the shift from conventional methodologies to contemporary deep learning techniques, specifically focusing on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The study examines key architectures including CNNs, and various Transformer-based models, analyzing their performance evaluating their effectiveness in diverse tasks such as image classification, object detection, and facial recognition. The research highlights the strengths and limitations of CNNs and ViTs, focusing on their ability to handle complex and diverse datasets. A detailed comparative analysis is conducted, emphasizing performance metrics, robustness, and adaptability across different image recognition scenarios. The results reveal that while CNNs excel in traditional image processing tasks, ViTs demonstrate significant improvements in capturing long-range dependencies, thereby enhancing recognition accuracy in more complex contexts. This analysis offers critical perspectives on selecting and applying image recognition models, guiding future exploration and practical use in various industries. It underscores the impact of deep learning innovations on advancing image recognition capabilities and highlights potential directions for ongoing development in the field.
BLE Bluetooth remote-controlled car based on ESP32
Authors: Tie Yuhe , Chen Peiming , Ma Yingqi
Published: November 2024

Abstract. With the rapid advancement of information technology, Bluetooth technology has been widely used in the field of short-range wireless communication, playing an important role in information exchange. As one of the main technologies for short-range wireless communication, Bluetooth technology has broad application prospects, especially in robotic applications such as remote-controlled cars. This paper provides a detailed introduction to the design and implementation process of a Bluetooth remote-controlled car system based on the ESP32. It covers the classic Bluetooth and Bluetooth Low Energy (BLE) functionalities of the ESP32, the hardware design and assembly of the car (including the DC motor drive module, Bluetooth module, and speed measurement module), and methods for establishing a connection between the ESP32 and Bluetooth module, data transmission, and remote control of the car's movement. The paper also evaluates the system's performance through experiments, discusses the application and parameter adjustment of the PID control algorithm in enhancing speed control, and finally summarizes the research findings and suggests directions for future work.
Research on CAPTCHA recognition technology based on deep learning
Authors: Tang Shengyuan
Published: November 2024

Abstract. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), a security technique widely used on the Internet, can be utilized to distinguish human users from automated programs. The rapid development of deep learning technology has led to the emergence of graph-based recognition techniques that demonstrate excellent performance, which has prompted the investigation of CAPTCHA recognition based on deep learning as a research area of significant interest. This paper addresses the challenging problem of CAPTCHA recognition based on deep learning techniques, reviews the development and classification of CAPTCHA, examines traditional CAPTCHA recognition methods, and delves into the application of deep learning in CAPTCHA recognition. Therefore, a CAPTCHA recognition system is designed and its effectiveness is verified through experiments. This paper makes a significant contribution to the field of CAPTCHA recognition by proposing a deep learning-based approach, which not only enhances the accuracy and efficiency of CAPTCHA recognition, but also provides new ideas and methods for the further development. In the future, further research will be conducted in the field of CAPTCHA recognition to explore additional deep learning models and techniques with the aim of boosting the security and user experience of CAPTCHA.
A modified extended Kalman filter based fusion of auto-correlation data for robot SLAM estimation
Authors: Li Gen
Published: November 2024

Abstract. With the popularization of robot technology, various technological developments related to robots have gradually entered a stage of rapid development. Especially path planning technology, which has been a very important and worthwhile part of robot motion and behavior since the concept of robots emerged. In this technology, various obstacles, environmental changes, and efficiency factors usually need to be considered. Whether in the past, present, or future, robot behavior pursues greater precision, reliability, adaptability to the environment, and even anthropomorphism. In order to achieve these goals, the idea of data fusion has emerged in the development of robot data processing. Therefore, the research direction is to use Kalman filtering for noise filtering and data correction to improve the accuracy of fused data. By using a feasible and strongly correlated data fusion method, combined with an improved extended Kalman filter, various data generated by robots can be fused in multiple dimensions and have correlations, reducing the negative impact of data differences and improving the quality and reliability of data fusion.
Object detection and tracking for drones: A system design using dynamic visual SLAM
Authors: Wu Dongli
Published: November 2024

Abstract. Drones can play a quite crucial role in many walks of life today. Enhancing the visual perception ability of drones is crucial to their intelligence level. Among them, it is necessary to focus on strengthening the detection, tracking and mapping capabilities of drones for dynamic objects. However, the existing visual SLAM systems carried by drones do not perform well in dynamic environments. This project designs a monocular visual SLAM system specifically for drones, aiming to achieve efficient three-dimensional mapping and target tracking, surpassing the limitations of simple static mapping and positioning. Besides, this project constructs a drone dynamic SLAM system developed on the ORB-SLAM3 structure, uses drone images to detect, track and map object motion models, and reconstructs environmental maps to obtain motion parameters with real physical scales. This project strives to optimize the input pre-processing module, improve the validity of data and output environmental maps and raster maps. The outcomes demonstrate the system's strong accuracy and adaptability in dynamic installation procedures.
Analytical Evaluation of MOSFET Secondary Effects
Authors: Gao Tianluo
Published: November 2024

Abstract. MOSFET is a field effect transistor that can be used in a wide range of analog and digital circuits. It has the advantages of low power consumption, high speed and high integration. Its abbreviation is MOS. During the operation of MOS tubes, there are some secondary effects that cannot be ignored in circuit analysis and there is a research gap in analyzing and evaluating these effects. Therefore, in this paper, we will analyze and evaluate the body effect, channel length modulation effect, subthreshold conductivity effect, and short channel effect among the secondary effects by reviewing the related literature and give some methods to minimize the impact of secondary effects on circuits. In this study, the secondary effects of MOS tubes are discussed in depth. In the combing of previous related studies, the above several common secondary effects of MOS tubes are summarized and inductively analyzed, and the effects of reduced body effect and channel length modulation effect on circuits are summarized and evaluated. These results provide important theoretical support and practical guidance for MOSFET research.
Harnessing Artificial Intelligence for Personalized Learning Pathways: A Framework for Adaptive Education Management Systems
Authors: Zhang Qi
Published: November 2024

Abstract. The integration of artificial intelligence (AI) into education is revolutionising how we deliver personalised learning, where curriculum, pedagogy and assessments are dynamically adjusted to meet the demands of each learner. In this paper, we examine the role AI can play in the construction of personalised learning pathways, and explore its applications for K-12 education, higher education and lifelong learning. We highlight that AI-driven systems such as Summit Learning, Pounce at Georgia State University, and IBMs Watson Talent are helping to measure dramatic improvements in student and employee outcomes, ranging from retention rates, academic performance, to engagement levels. We show how AI can enable student profiling, adaptive learning path generation, and ongoing assessment via the continuous feedback loops that are critical to transformative personalised learning. We also highlight some of the key challenges related to how education (eg, data privacy, scalability and equity), and showcase case studies and data from AI applications in different educational contexts to demonstrate the potential of personalised learning enabled by AI. In summary, this paper underscores the benefits and challenges of implementing AI in education, providing a perspective on how AI is likely to shape the future of learning.
The Logic and Application of Greedy Algorithms
Authors: Zhang Jierui
Published: November 2024

Abstract. Being easy to implement and offering faster solutions, the greedy algorithms have widely been used in solving combinatorial optimization problems in computer science and many practical applications, such as resource allocation and data compression. This paper aims at analyzing the definitions, theory, and application of greedy algorithms in computer science as well as their logic and efficacy in numerous situations. In this analysis, concepts such as the greedy choice property and optimal substructure are examined as essential to understanding how greedy algorithms operate. The provided analysis mainly assumes prior knowledge of concepts and problems like the Fractional Knapsack problem and the Shortest Path problem solution as well as some practical problems, for example, resource allocation and the exact coin change problem. Analytical data and real applications are used to analyze the performance of greedy algorithms vis-a-vis other categories, such as the dynamic programming paradigm. The results reveal that greedy algorithms have the potential for great efficiency while also being restricted by certain parameters. They are ideal in the following aspects pertaining to computational aspects, that is, speed, ease of implementation, and applicability to large datasets. But they do not necessarily promise strong optimality in certain situations. The conclusion notes that greedy algorithms deserve further applications when the specified conditions are met and states that the effectiveness of these conditions needs to be studied.
Adaptive Depth Estimation via SoftHebbLayer in a Hybrid ResNet-UNet Architecture
Authors: Deng Yuantao
Published: November 2024

Abstract. Depth estimation is a key task in computer vision, critical for applications such as autonomous navigation and augmented reality. This paper introduces a novel hybrid neural network that combines ResNet and UNet architectures with a SoftHebbLayer, inspired by Hebbian learning principles, to improve depth estimation from RGB images. The ResNet backbone extracts robust hierarchical features, while the UNet decoder reconstructs fine-grained depth maps. The SoftHebbLayer dynamically adjusts feature connections based on co-activation, enhancing the models adaptability to diverse environments. This approach addresses common challenges in depth estimation, including poor generalization and computational inefficiency. We evaluated the model on the DIODE dataset, achieving strong results in Mean Squared Error (MSE), which is 0.0800 and Root Mean Squared Error (RMSE), which is 0.2805, demonstrating improved accuracy in both indoor and outdoor scenes. While the model excels in precision, further refinement is needed to reduce computational overhead and improve performance in challenging environments. This research paves the way for more efficient, adaptable depth estimation models, with potential applications in mobile robotics and real-time edge computing systems.