bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Brain-Computer Interface Technology in Neurorehabilitation
Authors: Zhu Jingjing
Published: November 2024

Abstract. Neurorehabilitation is a very important area that aims at bettering lives of patients who have neurological disorders and have severely damaged motor functions and impaired communications abilities. Brain-Computer Interface (BCI) technology has emerged as one of the highly promising tools in the application of neurorehabilitation, having innovations in motor recovery, speech restoration, and independence for patients. This review presents the applications and efficiency of brain-computer interface technology in neurorehabilitation procedures related to high-incidence neurological disorders like stroke, traumatic brain injury, and spinal cord injury, and complex neurological disorders like amyotrophic lateral sclerosis, multiple sclerosis, and cerebral palsy. Among these, some BCI approaches, mostly based on electroencephalographic recordings, have shown promising potential for motor recovery, communication, and improvement of independence in patients. It discusses key modalities like motor imagery-based training, neurofeedback, and robotic assistance, together with the landmark studies proving their efficacy. Moreover, it pays attention to basic and clinical research and points out the challenges and future directions for the alleviation of limitations in BCI technology. Despite the fact that BCI technology still has some basic problems at this point in terms of signal acquisition, processing, and individual training, there is huge potential for application in developing neurorehabilitation and improving the quality of life of those patients who have undergone serious neurological injury.
Comparison of Gene-based and Hormonal Contraception Methods for Female Domestic Cats (Felis silvestris catus)
Authors: Feng Zhenxi
Published: November 2024

Abstract. with the growing population of stray and feral cats, traditional contraception methods like spaying and neutering, though effective, present limitations for Trap-Neuter-Return (TNR) programs in terms of cost, feasibility, and health risks. This article compares and contrasts gene-based and hormonal contraception methods in female domestic cats (Felis silvestris catus). The article delves into alternative non-invasive methods, particularly focusing on AAV9-fcMISv2 gene therapy and various hormonal methods such as Megestrol Acetate, GnRH agonists, and melatonin, and reviewing the advantages and drawbacks of these methods. The comparative analysis highlights the promising potential of gene-based therapies as a long-term solution for controlling stray and feral cat populations.
Human skin colour evolution and adaptation relates to folate and vitamin D
Authors: Li Xinrui
Published: November 2024

Human skin colour has evolved independently in different regions as an adaptation to varing geographic environments, closely related to ultraviolet radiation (UVR) exposure. For example, white skin color tends to become lighter at higher altitudes, UVR level typically decrease with altitude. Since human skin is directly exposed to sunlight, UVR significantly impacts its characteristics. This paper primarily examines the effects of UVR on skin and its relationship to the skin colour evolution through the lens of natural selection. Additionally, we explore the roles of folate and vitamin D in skin colour variation, noting that UVR influences their levels, which in turn can lead to health conditions. We conclude that folate, vitamin D, and UVR levels are critical factors in human skin colour evolution.
Optimizing Top-K query processing on GPU using data compression and pre-filtering
Authors: Yang Yiqing , Fu Yan , Liu Shuyong , Zhao Zhixiang , Zhang Guoyin
Published: November 2024

In large-scale data analysis, efficient Top-K query processing is critical for numerous applications in science, industry, and society. Traditional approaches often involve substantial data transfer and computational overhead, making it difficult to meet the scalability and efficiency demands of modern datasets. This paper proposes a GPU-accelerated Top-K query processing method that integrates data compression and pre-filtering techniques to address these challenges. By partitioning and compressing data on the host side, it alleviates common PCIe bottlenecks in heterogeneous computing environments. A metadata-driven pre-filtering technique further reduces the data volume processed on the GPU, significantly improving query performance, particularly when handling anti-correlated datasets. Experimental results demonstrate that this method markedly reduces data transfer and processing time, confirming its effectiveness in enhancing the efficiency and scalability of Top-K query processing compared to existing methods.
Exploring the Effects of Attention Mechanisms on Road Crack Detection and Classification
Authors: Peng Xingye
Published: November 2024

Pavement cracks are a prevalent form of roadway distress that pose significant safety hazards, necessitating prompt detection and repair. Due to the extensive road network, traditional image processing-based crack detection methods exhibit limitations in recognition accuracy and speed. To address these challenges, this paper proposes an efficient pavement crack detection model based on YOLOv8, termed YOLOv8-ACD (YOLOv8 - Attention for Cracks Detecting), which integrates a global attention mechanism. YOLOv8-ACD enhances detection efficiency and accuracy by focusing on crack identification while filtering out most irrelevant information. We evaluated YOLOv8-ACD on the RDD2022 dataset, and experimental results demonstrate significant improvements in key performance metrics, such as F1-Score and mean Average Precision (mAP), compared to the original YOLOv8 and other mainstream models. The real-time processing capability of this model makes it suitable for practical road inspection and maintenance, effectively reducing the workload of maintenance personnel and enhancing roadway safety.
Enhancing Image Stitching Algorithms with SIFT Feature Detection
Authors: Wang Mingkai
Published: November 2024

Image stitching technology plays a significant role in the fields of computer vision and image processing, with applications ranging from panoramic photography to virtual reality (VR), augmented reality (AR), medical diagnostics, and autonomous vehicle technology. As technology advances, the demand for high-quality, real-time panoramic images provided by image stitching technology continues to grow. This study aims to implement an image stitching method based on the Scale-Invariant Feature Transform (SIFT) feature point detection algorithm, combined with the Random Sampling Consensus (RANSAC) algorithm and the calculation of the homography matrix to automatically stitch two images. This paper elaborates on the entire process of image feature extraction, feature matching, homography matrix calculation, and image fusion, and compares different fusion modes. The experimental results show that the method can achieve seamless image stitching in some cases, its performance in complex scenes such as crowds or traffic flows is average. This study provides new perspectives and methods for the application of image stitching technology.
Advancements in EEG-Based Brain-Computer Interface Integrations for Stroke Rehabilitation
Authors: Zhuang Zhengyu
Published: November 2024

Stroke is a leading cause of long-term disability, hindering mobility and independence. Traditional rehabilitation methods may not fully address the diverse needs of stroke survivors, prompting the exploration of advanced technologies such as Brain-Computer Interfaces (BCIs) to enhance rehabilitation performance. The works highlights the integration of Electroencephalogram (EEG)-based BCIs with functional electrical stimulation (FES), sensory stimulation technologies like augmented reality (AR) and virtual reality (VR), and robotic systems. These integrations demonstrate potential in enhancing muscle strength, coordination, and motor function through targeted muscle stimulation. The combination with AR/VR provides immersive training environments, improving motor learning and cognitive engagement. BCI-robotic systems offer a closed-loop feedback mechanism, translating brain signals into robotic movements, which aids in motor assistance and functional recovery. Despite the potential of these integrated systems, challenges remain, including the complexity of signal processing, user fatigue, system calibration, and the high cost of technology. Additionally, the need for robust machine learning (ML) and deep learning (DL) algorithms, user-friendly interfaces, and hardware compatibility is crucial for wider clinical adoption.
Innovative Non-Invasive Brain-Computer Interfaces for Addressing Cognitive Decline in the Elderly
Authors: Zhang Yutong
Published: November 2024

The problem of cognitive decline in the elderly is becoming increasingly crucial as the population ages. It is normal to experience some degree of cognitive decline as one ages. However, severe cognitive decline can lead to some elderly individuals being unable to engage in independent daily life. Thus, it is important to research strategies for enhancing cognitive capacities or preventing cognitive decline, particularly those related to Brain-Computer Interface (BCI) technology. Functional Near Infrared Spectroscopy Imaging (fNIRS)-BCI, for instance, is real-time, portable, non-invasive, and appropriate for mobile environments. However, its spatial resolution is low, limited by its light penetration ability, making it difficult to monitor deep brain activity. On the other hand, sophisticated brain-computer interfaces and complicated brain function studies can benefit from functional Magnetic Resonance Imaging (fMRI)-BCI due to its high spatial resolution and wide coverage. However, the equipment is expensive, bulky, and requires a static environment with poor real-time performance. Transcranial Doppler (TCD)-BCI is appropriate for risk assessment and cerebral blood flow monitoring as it is portable, non-invasive, and able to track changes in blood flow in real time. However, monitoring only blood flow velocity indirectly monitors brain activity, limiting its application scope. This article will introduce the research and application of three different types of non-invasive BCI in improving cognitive decline in the elderly, as well as their respective advantages and disadvantages.
Analysis of VR and AR Applications in Various Fields based on HCI Principles
Authors: Huang Luyizhe
Published: November 2024

Due to the continuous development of hardware and software, Virtual Reality (VR) and Augmented Reality (AR) technologies are rapidly maturing and becoming popular in people's lives. However, people do not have much knowledge and understanding of them. In order to enable more people to understand Virtual Reality and Augmented Reality and distinguish between them, this article will provide some basic explanations of VR, AR, and HCI. Through specific cases, the application of VR and AR in urban cultural creative design, education field, business field, game field, tourism and cultural experience, and manufacturing industry will be presented and analyzed. In addition, how they are applied based on Human-Computer Interaction (HCI) principles will be discussed separately. Furthermore, the feasibility of this technology and its impact on the industry were discussed. Finally, the limitations of VR and AR technologies were discussed, and some solutions were proposed. The future development directions of VR and AR technologies were also discussed.
Exploring the Validity of Knockoff Nets Model Stealing Attack on Vgg16 Based on Different Models
Authors: Hei Yunxi
Published: November 2024

Model stealing attacks represented by the Knockoff Nets method steal the intellectual property of AI models by black-box querying. Model stealing attacks on a wide range of deep learning models have attracted widespread attention in recent years. However, there has not been any research on stealing attacks based on common models such as VGG16, ResNet18, AlexNet, etc., especially since the research on the validity of the attack on the VGG16 model is still insufficient. Therefore, in this paper, three types of models, VGG16, ResNet18, and AlexNet, are used as the models for stealing, and the Knockoff Nets method is used to carry out stealing attacks on the pre-trained model of VGG16, which is capable of cat and dog image recognition. This paper analyzes the stealing similarity, stealing model accuracy and stealing training time so as to reflect the validity of stealing. The paper shows that Knockoff Nets based on three types of models, VGG16, ResNet18, and AlexNet, are all effective against the VGG16 model stealing attack, and the more similar the architectures of the stealing model and the victim's model are, the better the stealing effect is. In addition, to a certain extent, the stealing training time and the stealing model accuracy are affected by the architecture of model used to steal. This paper reveals the validity of the Knockoff Nets model stealing attack against VGG16 based on three types of models, namely VGG16, ResNet18, and AlexNet, to provide a reference for model security protection.
Application of Artificial Intelligence Methods in Knowledge Graphs
Authors: Yu Sijiu
Published: November 2024

This paper mainly explores the application of artificial intelligence (AI) technologies in knowledge graphs (KGs), focusing on how natural language processing (NLP), machine learning, and deep learning methods can achieve the automated construction of KGs. First, the paper introduces the basic concepts of KGs and the limitations of traditional construction methods. Then, it analyzes recent technological advancements in knowledge graph construction, data fusion, and reasoning, with particular emphasis on the application of graph convolutional neural networks (GCNs) in handling multi-relational data. Finally, the practical applications of KGs in business analytics, healthcare information systems, and recommendation systems are discussed, demonstrating their broad potential in data management and reasoning.
Rapid Delivery System of Emergency Medical Supplies Based on UAV Robot
Authors: Li Yike
Published: November 2024

This paper reviews the rapid advancements and innovations in Unmanned Aerial Vehicle (UAV) technology, focusing on their application in emergency medical supply delivery. It highlights key developments in lightweight materials, power systems, modular designs, and multifunctional use, which have significantly enhanced UAV capabilities. Case studies from various countries demonstrate UAVs' effectiveness in delivering medical supplies during natural disasters and crises. The paper also explores emerging technologies such as autonomous operation, 5G communication networks, and integrated healthcare platforms, which are improving the reliability and safety of drone-based deliveries. The review provides insights into the current challenges, such as technical limitations and regulatory hurdles, while identifying potential pathways for future advancements.
AI-Based Supply Chain Risk Management
Authors: Mo Xianyu
Published: November 2024

As the complexity of global supply chains increases, supply chain risk management becomes increasingly important. The development of artificial intelligence (AI) technology provides new tools and methods for managing supply chain risks. This paper aims to review the application of AI in supply chain risk management, exploring current research progress, challenges, and future development trends. Through a systematic literature search and analysis, this paper examines relevant papers from databases such as Google Scholar, Web of Science, EI, and Scopus, discussing the specific applications of machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms in supply chain risk management. The study finds that these AI technologies demonstrate significant advantages in risk prediction, anomaly detection, image recognition, text mining, supply chain optimization, and emergency strategy formulation. However, issues related to data privacy and security, technical complexity, and implementation difficulties remain major challenges in current applications. In the future, as AI technology advances and interdisciplinary integration develops, supply chain risk management will have more opportunities. This study provides specific application suggestions for enterprises and decision-makers and points the way for future research.
Research and Analysis on the Application of Radio Frequency Identification Technology in the Internet of Things and Future Development
Authors: Zhang Zikai
Published: November 2024

In the past five years, 5G technology has been widely used, with its development and popularity. "Internet of Everything", that is, through the 5G technology will be all aspects of life together, this concept gradually into the people's field of vision, in a series of 5G-related technology, radio frequency identification technology as one of the important thing Internet technology has received widespread attention. This article will be based on a combination of a literature review and case study method, a brief introduction to RFID technology and its application in the Internet of Things in the current situation and future development of the analysis. This paper provides some inspirations and suggestions for the practitioners in the field of Internet of Things (IoT) through in-depth discussion of the application of RFID technology in IoT; by analysing the application cases and challenges of RFID technology, it can provide important references for the promotion of intelligent development of Internet of Things (IoT).
Progress and Application of Unsupervised Feature Extraction Methods
Authors: Ma Jingxuan
Published: November 2024

Unsupervised feature extraction is crucial in machine learning and data mining for handling high-dimensional and unlabeled data. However, existing methods often ignore feature relationaships, resulting in suboptimal feature subsets. This paper reviews the current state of unsupervised feature extraction methods, discussing the limitations of traditional methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), particularly in terms of interpretability, sensitivity to outliers, and computational resource challenges. In recent years, improvement strategies such as information theory, sparse learning, and deep learning (e.g., deep autoencoders and generative adversarial networks) have significantly progressed in feature extraction. This paper analyzes the practical applications of these methods in image processing, gene analysis, text mining, and network security. For example, in image processing, deep autoencoder-based methods such as Matrix Capsules with EM Routing can effectively extract key features from complex images. In text mining, unsupervised feature selection methods combined with generative adversarial networks significantly improve the efficiency of processing high-dimensional text data. Additionally, this paper explores future research directions such as multimodal data processing, improving real-time processing capabilities, and integration with other machine learning techniques (e.g., reinforcement learning, transfer learning), providing insights for further development of unsupervised feature extraction technologies.
A Review of The Application of Natural Language Processing in Human-Computer Interaction
Authors: Song Jindi
Published: November 2024

Over the past few decades, advancements in computing technologies have propelled research in Human-Computer Interaction (HCI). Recently, the rise of artificial intelligence, particularly deep learning and pre-trained models like BERT and GPT, has revolutionized Natural Language Processing (NLP), making HCI applications more intelligent and personalized. NLP has emerged as a critical technology in enhancing HCI, enabling more intuitive and efficient communication between users and machines. This review explores the various applications of NLP in HCI, highlighting its significant role in user interface design, chatbots, and virtual assistants. Specifically, the paper examines how NLP techniques such as intent recognition, sentiment analysis, and language generation contribute to the creation of more responsive and user-friendly interfaces through voice input, personalized experiences, and optimized feedback mechanisms. Furthermore, the challenges and limitations of implementing NLP in HCI are discussed, particularly concerning data privacy, model effectiveness, and the ethical implications of deploying NLP systems, with a focus on privacy and trustworthiness. Finally, the paper considers future research directions in this field, emphasizing the importance of interdisciplinary collaboration in overcoming current barriers and enhancing the applicability of NLP in real-world contexts, while urging developers to prioritize responsible AI practices in their designs.
Research Progress and Key Technology Analysis of Lower Limb Rehabilitation Robots
Authors: Cao Qingze
Published: November 2024

In China, there is a large number of patients with lower limb movement disorders and limited medical resources, while the market for rehabilitation technologies is growing rapidly. The technology of lower limb rehabilitation exoskeleton robots has been successfully applied and shows significant development potential. Both domestic and international research on lower limb rehabilitation exoskeleton robots focuses on the design aspects of comfort, bionics, lightweight construction, and safety. Notable domestic and international companies and universities are committed to enhancing personalization and human-machine interaction experiences, reducing costs, and expanding application fields. A preliminary analysis of the forward and inverse kinematics of lower limb rehabilitation exoskeleton robots has been conducted to derive the kinematic equations and velocity Jacobian matrices, facilitating better optimization of the robot's control algorithms. Existing control algorithms are diverse, including position-based, force information-based, bioelectric signal-based, and intelligent control strategies, each with its own advantages and disadvantages. Among these, control algorithms that integrate artificial intelligence and other cutting-edge technologies hold the most promise. As the aging population continues to grow, this technology is expected not only to play a role in rehabilitation therapy but also to expand into other areas, such as assistive walking, along with the optimization of mechanisms and the evolution of control algorithms.
Research on Detection Methods for Text Generated by Large Language Models Based on Multi-Model Ensemble
Authors: Tian Liang , Jiang Nan
Published: November 2024

The rapid development of Large Language Models (LLMs) has made their generated text almost indistinguishable from human writing, posing significant challenges to traditional human-machine recognition techniques. This paper proposes a detection method based on multi-model ensemble to accurately identify text generated by LLMs. Firstly, a large-scale, diverse, and heterogeneous dataset is constructed, covering student writings and texts generated by models such as GPT-3, GPT-2, CTRL, and XLM. Then, a multifaceted detection framework integrating linear models, deep learning models, and pre-trained language models is designed. The linear model utilizes an argumentative essay dataset (DAIGT V2 Train Dataset) similar in distribution to the competition dataset, combined with adaptive BPE tokenization, N-Gram, and TF-IDF features. It employs Multinomial Naive Bayes and SGDClassifier to train classifiers that capture shallow statistical features of the text. The deep learning model fine-tunes the DeBERTa-v3-small model on large-scale datasets (Pile, Ultra, Human vs. LLM Text Corpus) to learn deep semantic representations of the text. The pre-trained language model introduces a fine-tuned DistilRoBERTa model, enhancing detection capabilities using third-party datasets. Finally, the above models are integrated through a weighted average strategy, significantly improving the generalization and robustness of the detection results. Experimental results show that this method achieved a score of 0.967466 in the Kaggle competition, earning a silver medal and outperforming any single model. The study demonstrates the effectiveness of multi-source data and multi-model ensemble in detecting LLM-generated text, providing new ideas and practical references for research in this field.
Beyond Pixels: The Journey to Realistic NPCs in Gaming
Authors: Pang Ye
Published: November 2024

With the rapid development of the gaming industry, players increasingly demand realism and immersion. The anthropomorphization of NPCs (Non-Player Characters) is crucial for enhancing the gaming experience. This article reviews the development of NPC anthropomorphization technologies, analyzes representative methods and cases, and discusses current limitations and future trends. The article traces the evolution of NPC technologies from early rule-based systems to modern deep reinforcement learning and interdisciplinary approaches, highlighting key milestones. It assesses the advantages and limitations of these technologies at various stages, evaluating their impact on the gaming experience and related fields. Additionally, the article explores current trends in NPC anthropomorphization, particularly the integration of biology, neuroscience, psychology, and computer science, and analyzes the potential and challenges of these emerging methods. Looking forward, the article suggests possible breakthroughs and research focuses for NPC anthropomorphization technologies, offering insights for researchers and practitioners. This study aims to promote innovation in this field, providing players with a more realistic and immersive gaming experience. Furthermore, advancements in NPC technologies will also enhance AI research, driving it towards higher intelligence and broader applications.
Research on Image Feature Extraction Based on Convolutional Neural Network
Authors: Xiang Siqi
Published: November 2024

Abstract. This article explores the impact of various hyperparameters on the performance of image feature extraction using convolutional neural networks (CNNs), with a focus on learning rate, dropout rate, batch size, and the number of epochs. Using the CIFAR-10 dataset, extensive experiments were conducted to optimize these parameters, aiming to achieve high accuracy while avoiding overfitting. The findings underscore the importance of carefully selecting these hyperparameters to balance training efficiency and model performance. Through a rigorous analysis of the effects of these hyperparameters on model performance under various configurations, including training accuracy, test accuracy, training loss, and test loss, our experimental results indicate that the model achieves optimal performance with a learning rate of 0.0001 and a dropout rate of 0.5. The model demonstrates optimal performance in avoiding overfitting when the number of training epochs is set to 10. Additionally, although batch size has a relatively minor effect on overall model optimization, a slight improvement in performance was observed when the batch size was set to 32.
AI and the Quest of Trustworthy Information
Authors: Lai Bokai
Published: November 2024

Abstract. Artificial Intelligence (AI) is revolutionizing the sectors of medicine and media by improving productivity, precision, and customization. AI is revolutionizing the development, distribution, and consumption of material in the media while also being essential in information verification and moderation. AI in medicine enhances clinical processes, personalizes patient care, and increases diagnostic accuracy. But these developments also bring with them new difficulties, including preserving media originality and guaranteeing the accuracy and openness of AI-generated insights in the medical field. In order to comprehend their impact on content creation and information distribution, this article investigates the approaches and uses of AI, such as Generative Adversarial Networks (GANs) and the Information Adoption Model (IAM). In media, AI automates video editing, content moderation, and tailored recommendations, while in healthcare, it enhances diagnostic accuracy, personalizes care, and simplifies clinical operations. The article also discusses issues like biases, the necessity of human oversight, and data privacy, highlighting how crucial it is to create AI systems that are both morally and practically sound.
Application of Capsule Network in Text Classification Problem
Authors: Yang Shengtao
Published: November 2024

Abstract. The rise of the Internet and the big data era has led to a significant increase in text data, making text classification an essential task in natural language processing. Conventional deep learning models have advanced considerably in understanding text context dependencies; however, they still struggle with long-range dependencies and multi-scale features. In recent years, capsule networks (CapsNets) have demonstrated outstanding results in the area of image recognition due to their unique structure and dynamic routing mechanism, which has inspired researchers to explore their application in text classification tasks. This article examines the advancements in software and research related to CapsNets in the context of text classification challenges. Through its capsule units and dynamic routing algorithms, capsule networks can better express the part-whole hierarchical structure of text and effectively capture the complex relationships in text. Compared with traditional neural networks, capsule networks have shown advantages in dealing with both tasks involving text classification with either a single label or multiple labels. In addition, this paper also examines ways to optimize and enhance CapsNets for text classification, such as incorporating attention mechanisms to improve the model's capacity to recognize important information, as well as refining routing algorithms.
Improving Model Robustness through Hybrid Adversarial Training: Integrating FGSM and PGD Methods
Authors: Zhong Zeshan
Published: November 2024

Abstract. With the widespread use of deep learning models in various applications. People are gradually realizing the vulnerability of these models to adversarial attacks. Adversarial training is an effective strategy to defend against adversarial attacks. Based on the advantages and disadvantages of the current mainstream Fast Gradient Sign Method (FGSM) adversarial training and Projected Gradient Descent (PGD) adversarial training, this paper proposes a hybrid adversarial training that integrates FGSM and PGD methods and uses the ResNet-18 model and SVHN dataset for testing. Experimental results show that hybrid adversarial training can effectively reduce training time. Its accuracy on the original data set is higher than that of PGD adversarial training, which is improved by about 2%. The performance when facing FGSM attacks is almost the same as that of single FGSM adversarial training. The performance when facing PGD attacks decreases more significantly, which is about 2% to 3% lower than that of PGD adversarial training. This study not only helps to understand the robustness of hybrid adversarial training to models facing adversarial attacks but also helps in studying new adversarial training strategies.
Prediction of the Number and Trend of Olympic Medals Won by Various Countries Based on Neural Networks
Authors: Song Zicheng
Published: November 2024

Abstract. As the world's largest multi-sport event, the Olympic Games attract top athletes from all over the world to compete for the world No. 1 crown. The total number of medals in each Olympic Games is the most intuitive data to measure the sports strength of a country. Using deep learning technology, the total number of medals in previous Olympic Games can use to predict the number of medals that countries will receive in future Olympic and analyze their trends,which is helps for Countries understand the strengths and weaknesses of their athletes in various sports, and also provide a reliable reference for countries in the future development direction of sports .Based on the analysis of the total number of medals won by athletes from various countries in the Olympic Games, a deep learning model is constructed and trained based on Python, and its hyperparameters are adjusted. The mean square error (MSE) and coefficient of determination (R) are output as the evaluation criteria for the prediction results. The effectiveness and accuracy of the model were verified by making specific predictions of the previous Olympic performance of four countries: United States, Germany, China, and Japan.Medal prediction based on neural networks not only provides data support for the formulation of sports strategies in various countries but also promote the development of sports data analysis. The prediction model of the change trend of the Olympic medal count will provide a new research perspective for the development of neural networks.
Analysis of the Applications of Algorithm and Automatic Pathfinding
Authors: He Weikun
Published: November 2024

Abstract. The current development of autonomous driving technology is very hot, which involves two fundamental aspects: one is the operating system as the foundation, and the other is the algorithm application. The theme of this review is to study the related algorithmic technologies and combine them with one of the key functions of autonomous driving: autonomous routing. The review discusses the application direction and environment of this function, and involves the use of algorithms in the backend. Autonomous routing is a key concept in multiple technical fields and plays an important role in helping entities effectively navigate complex environments. This review centers on the concept of autonomous routing and focuses on its application direction, usage environment, and supporting algorithms. The core research question is autonomous routing and its working principle. The review analyzes the main application scenarios of autonomous routing, such as autonomous driving and game development, and explores the algorithms commonly used in these scenarios. By conducting a comprehensive analysis of the main usage environments and algorithm structures, the review provides insights into the current state of autonomous routing technology. The research findings show that autonomous routing technology has been deeply embedded in multiple industries and is continuously expanding as the demand for technology grows. Furthermore, the review explores the potential future development of autonomous routing, anticipating that it will further develop in responding to various new challenges and opportunities.
A Survey of Hallucination Problems Based on Large Language Models
Authors: Liu Xinxin
Published: November 2024

Abstract. Large language models (LLM) have made significant achievements in the field of natural language processing, but the generated text often contains content that is inconsistent with the real world or user input, known as hallucinations. This article investigates the current situation of hallucinations in LLM, including the definition, types, causes, and solutions of hallucinations. Illusions are divided into different types such as factual and faithful, mainly caused by factors such as training data defects, low utilization of facts, and randomness in the decoding process. The phenomenon of hallucinations poses a threat to the reliability of LLM, especially in fields such as healthcare, finance, and law, which may lead to serious consequences. To address this issue, this article investigates methods such as managing training datasets, knowledge editing, and enhancing retrieval generation. Future research should classify and evaluate illusions more finely, explore multimodal strategies, enhance model stability, and integrate human intelligence and artificial intelligence to jointly address challenges, promoting the continuous progress of LLM.
Credit Evaluation System Based on FICO
Authors: Li Yiming , Shi Yanqin
Published: November 2024

Abstract. As credit transactions become more prevalent, financial institutions require effective methods to assess credit risk and reduce the likelihood of borrower default. In the U.S., the Fair Isaac Credit Organization (FICO) score is widely used by banks and insurers to evaluate personal creditworthiness. This paper aims to develop an automated credit scoring tool based on the FICO system to help financial institutions improve risk assessment. The paper leverages the random forest algorithm for data preprocessing and feature engineering to extract key variables, such as the borrower's financial status and credit history. To ensure data stability and interpretability, Information Value and Weight of Evidence techniques are applied to process these variables. Additionally, the Sigmoid function is used to map the model output to a range between 0 and 1, making it suitable for generating credit scores. This random forest algorithm helps handle non-linear relationships and missing data, while cross-validation enhances the models generalization ability. After training, the paper achieved an automated credit scoring system, closely aligned with the FICO scoring system. The models Area Under the Curve (AUC) value reached 0.84, indicating strong predictive accuracy and reliability. This tool enables financial institutions to more accurately assess credit risk, offering a robust, data-driven approach to improve decision-making and risk management.
Predictive Modeling in IoT-Driven Agriculture: A Comparative Study of Regression and Classification with LIME Interpretability
Authors: Zhang Zhanfei
Published: November 2024

Abstract. This study aims to compare the performance of regression and classification machine learning models in crop prediction by analyzing data from IoT-driven agriculture. Linear regression and random forest regression models were used to predict the percentage of root growth dry matter, while logistic regression and support vector machines were employed to classify crop production regions. To enhance model interpretability, the LIME tool was applied to analyze feature importance. The experimental results demonstrate that the models perform well in terms of prediction accuracy, and LIME provided clear feature explanations, helping identify the variables with the greatest impact on prediction outcomes. This research offers data-driven insights for optimizing resource management in smart agricultural systems.
Accurate Target Positioning and Detection Algorithm Integrating Deep Learning and Prior Knowledge
Authors: Liu Ye , Song Haisheng
Published: November 2024

Target targeting and detection is one of the core tasks in the field of computer vision, which has great significance for autonomous driving, intelligent surveillance, medical image analysis and other fields. In this paper, we propose an accurate target positioning and detection algorithm integrating deep learning and prior knowledge, aiming to combine the high efficiency of Faster R-CNN algorithm and the accuracy of prior knowledge to improve the accuracy and efficiency of target detection. Experimental results show that the proposed method achieves excellent performance on several datasets.
Investigating the Impact of Age and Education on Delay Discounting: A Predictive Random Forest Model for Personalized Marketing and Mental Health Risk Detection
Authors: Lu Huazhang
Published: November 2024

This study uses a random forest model to predict delay discounting rates, aiming to support mental health interventions and personalized marketing strategies. Delay discounting refers to the tendency to prefer smaller immediate rewards over larger delayed rewards, a behavior closely linked to impulsivity and relevant in both psychological and commercial contexts. Data from Garofalo et al., including 357 healthy Italian adults categorized by age and education level, were used. Two-way ANOVA showed that education significantly influences delay discounting rates, while age does not. The model was utilized to anticipate delay discounting behaviors influenced by various factors. The random forest algorithm reached an impressive accuracy rate of 92%, showing particular proficiency in forecasting both high and low delay discounting classifications. Additionally, the model successfully pinpointed individuals whose actual discounting rates diverged significantly from the predictions, offering crucial insights for proactive mental health interventions. Based on the predictions, personalized strategies were recommended for individuals with different delay discounting rates, demonstrating the value of this predictive model in the fields of mental health and commercial marketing.
Network Security Applications in the Internet of Things
Authors: Zhou Juncheng
Published: November 2024

In the realm of Internet of Things (IoT) network security, encryption algorithms serve as one of the core means to protect data and device security. Given the extensive connectivity of IoT devices to the Internet, ensuring security becomes a paramount challenge. Encryption algorithms effectively guarantee confidentiality, integrity, and authentication during data transmission while thwarting malicious attackers from unauthorized access or tampering. Therefore, this paper proposes employing hash storage for encrypting IoT data to enhance its protection. The subsequent sections will demonstrate how the data is stored and encrypted through code implementation. The method adopted in this research involves writing code and testing code to assess its reliability in terms of security measures. This investigation reveals that utilizing hash storage encryption of data provides a robust means for managing data securely within an IoT framework; moreover, longer encrypted data lengths contribute to enhanced security levels with the adoption of the SHA-256 algorithm.
Distribution-Aware Dual-LLM Collaborative Framework for Human Preference Prediction
Authors: Tian Liang , Jia Shiguang
Published: November 2024

This paper proposes a novel Distribution-aware Dual-LLM Collaborative Framework (D2CF) for human preference prediction in large language model dialogue systems. Through data analysis in the Kaggle LMSYS Chatbot Arena competition, we innovatively selected two complementary base models: Gemma-2-9b and Llama-3.1-8b. The framework's main technical innovations include: (1) A model complementarity quantification method based on Wasserstein distance, optimizing model selection from a data distribution perspective; (2) A parameter-efficient QLoRA improvement strategy that reduced computational overhead by 42.6% through adaptive rank adjustment and quantization optimization; (3) A validation set-driven dynamic weight fusion mechanism that achieves adaptive feature fusion through attention mechanisms. In the competition evaluation, this solution achieved stable performance on both public and private test sets, ultimately winning a silver medal, validating the effectiveness and robustness of distribution-aware strategies in practical applications. The significant performance improvement from public to private test sets demonstrates the superiority of this method in handling different data distributions. This paper details the technical principles and implementation details of the solution, providing reproducible engineering practice references for human preference prediction tasks in large-scale language models.
Analysis of the Structure Characteristics and Influencing Factors of the Global Flower Trade Network
Authors: Zhang Xiangmin , Zhan Fuchun
Published: November 2024

Global flower trade plays an important economic, cultural and environmental role in international trade, so it is of great significance to construct and analyze flower trade network. Through the construction of the global flower trade network from 2014 to 2023, this paper analyzes its structural characteristics and space-time evolution law, and finds that the global flower trade network has an obvious core-edge structure, and core countries such as the Netherlands, Germany and the United States occupy a dominant position in the trade, and the network stability is high. Then, from the perspective of flower planting, the explanatory variables were set in combination with flower transportation, national politics, economy and culture, etc., and the influencing factors of global flower trade were analyzed using the QAP model. The regression results showed that economic development level and geographical proximity had a significant positive impact on flower trade, while water resource richness had a negative impact, and language similarity promoted trade development.
Research on Quantum Computing Acceleration of Support Vector Machines in Multi-dimensional Nonlinear Feature Spaces
Authors: Liu Haozhe
Published: November 2024

The current development of Support Vector Machines (SVM) has reached a bottleneck, with issues such as long training times and weak interpretability when dealing with large-scale, multi-dimensional data. This paper introduces the concept of Quantum Support Vector Machines (QSVM) and achieves efficient solutions through quantum algorithms such as the HHL algorithm. The research designs a computational architecture that combines classical and quantum computing, utilizing the Pauli decomposition of Hermitian matrices to simulate the quantum simulation of Hamiltonian quantities and implementing the quantum simulation of unitary operators. Based on the conclusions, a complete quantum linear solver circuit is designed, achieving exponential growth in computational complexity. Experiments using the Iris dataset demonstrate the excellent classification performance of QSVM, which outperform classical SVM in classification accuracy, computational time complexity, and memory requirements. More efficient quantum mapping algorithms and quantum circuit optimization methods are implemented, providing new ideas and methods for the application of SVM to large-scale datasets.
The Role of CNN in Soil Detection
Authors: Huang Kuan , Hu Yan , Liu Shiyue
Published: November 2024

Soil is an important part of agricultural production and environmental health, and its physical and chemical properties directly affect crop growth and ecosystem stability. However, traditional soil testing methods such as gravimetric and time-domain reflectance (TDR) techniques, although accurate, have limitations such as being time-consuming, costly and complicated to maintain. To improve the quality of soil, the researchers proposed the method of combining Convolutional Neural Network (CNN) convolutional models with soil detection. In this paper, this will be collated and the measurements will be classified into three categories: soil water content detection based on CNN, soil contamination detection based on CNN, and soil microbial detection based on CNN models. Such classification can be effectively combined with hybrid models such as CNN, Gate Recurrent Unit (GRU) and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), among others. The results show that the hybrid model has higher accuracy and stability in predicting soil water content, microbial activity and heavy metal contamination, which helps to improve soil health monitoring and management. Future research can further combine multiple models to improve the accuracy and application range of soil testing.