bluuebunny/crossref_metadata_2025 huggingface dataset:
Causal Inference in Natural Language Processing: Application Status and Future Outlook
Authors: Chu Yifan
Published: November 2024

Abstract. Causal reasoning has received much attention in recent years. Unlike statistical learning that focuses on the correlation between variables, causal inference can analyze the causality between variables and avoid false causal relationships caused by confounding factors. The use of causal inference to help solve Natural Language Processing (NLP) problems has made great progress. Most existing studies have focused on exploring the application of causal relationships in downstream tasks of NLP, achieving good results. This article conducted a comprehensive survey, focusing on investigating the advantages of causal inference methods in solving specific NLP problems and applications, as well as their practical advantages compared to deep learning methods. Intended to provide researchers in the NLP field with more detailed perspectives and recommendations. Specifically, this article first introduces the relevant concepts of causal inference, including causal inference and related concepts of causal relationships, as well as the differences between causal inference and statistical machine learning; Then, existing research on the use of causal relationships in downstream applications was summarized, including bias removal, stock price prediction, document dialogue based, and continuous few shot learning. Finally, a summary was made on the development of future causal relationships.
Enhancing Black-and-White Image Colorization Using Cycle GAN: A Study on Unpaired Image Translation
Authors: Ren Hengrui
Published: November 2024

Abstract. This paper explores the application of Cycle-Consistent Generative Adversarial Network (Cycle GAN) for the colorization of black-and-white images, addressing the challenges in image to the image translation and it was not requiring paired datasets. Cycle GAN, a generative adversarial network, uses cycle consistency loss to map grayscale images to color, effectively preserving structural integrity. The model is trained on unpaired datasets containing black-and-white and color images, enabling it to generate colorized versions from grayscale inputs. Performance evaluation is conducted using Two significant evaluation metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Results demonstrate that Cycle GAN effectively preserves image structure and detail, achieving high PSNR and SSIM values, particularly in scenes with distinct color boundaries. However, challenges arise in complex and low-resolution scenes where color accuracy and detail reconstruction may degrade. Additionally, this paper assesses the impact of different loss functions, learning rate schedules, and training strategies on the models output quality. Future work will focus on addressing these limitations by incorporating attention mechanisms and other advanced techniques to improve Cycle GAN's ability to handle intricate image features and complex scenes.
AI integration in creative industries: Challenges and opportunities
Authors: Xue Fei
Published: November 2024

Abstract. This paper delves into the profound impact of Artificial Intelligence (AI) on the film and creative industries, with a focus on AI-driven content creation, audience engagement, market analytics, and the ethical considerations that accompany technological integration. Through detailed analysis of specific applications, such as scriptwriting, visual effects, personalized content, and recommendation systems, the study reveals how AI technologies are reshaping traditional creative processes and audience interaction. It also addresses the implications of AI on employment within creative sectors, intellectual property, authorship rights, and the importance of cultural sensitivity in AI applications. By examining both the opportunities and challenges presented by AI, the paper aims to provide a balanced view on the future of work in creative industries and the ethical framework needed to guide the responsible use of AI technologies.
Design and optimisation of a two-stage amplifier based on a differential input stage and a common-source amplifier
Authors: Chen Zitao
Published: November 2024

Abstract. Since the emergence of integrated circuits, through the circuit structure, the production process of continuous iterative updating, so that its used in various types of electronic equipment more and more widely. Currently, operational amplifier circuits play an extremely important role in signal processing and communication systems. Amplifiers have different characteristics depending on the needs of different electronic devices. This thesis addresses the optimisation and design of the characteristics of the two-stages amplifier. Firstly, several classical amplifier structures are analysed. Select the appropriate structure and combine it into a two-stages amplifier. Simulation is carried out on cadence software and later the simulation results are analysed before parameter optimisation. On this basis, a two-stages amplifier based on a PMOS differential input stage with a common-source amplifier has been designed. It also improves the amplifier's gain, frequency range, gain bandwidth, and other performance metrics. Using PMOS self-bias circuit to improve power supply stability. Finally, based on the development hotspots of integrated circuits, the possibility of circuit optimization was analyzed. Discussions were conducted on negative feedback circuits, phase compensation methods, and other aspects.
Stock price prediction based on hybrid CNN-LSTM model
Authors: Zhao Qinghe , Hao Yue , Li Xuechen
Published: November 2024

Abstract. Stock price prediction is of great significance but faces numerous challenges. In this study, a hybrid CNN-LSTM model was utilized. The stock data were processed and feature engineering was conducted. The model architecture and training strategies were expounded. Experimental results demonstrated that this model outperformed traditional methods and benchmark models on the test set, featuring strong capabilities in feature extraction and handling long-term dependencies. Key roles of data preprocessing, hyperparameter adjustment, and model fusion were also summarized, providing valuable references for stock price prediction.
An Analysis of the Future Trends and Challenges of Autonomous Driving Technology
Authors: Wang Xujun
Published: November 2024

Abstract. Autonomous driving technology is one of the most groundbreaking innovations in the automotive industry in recent years. This article will explore the latest trends in autonomous driving, analyzing its historical development, current status, and future prospects, while also assessing its potential across different application scenarios. With the support of data and illustrations, this article aims to provide a comprehensive perspective to understand the development and challenges of this technology. As technology progresses, autonomous driving will significantly enhance traffic safety, efficiency, and convenience, ultimately leading the transportation industry into a new era. Although current autonomous driving systems still face challenges such as boundary identification and safety concerns, ongoing research and innovation across various fields will drive continuous improvements. In the future, autonomous driving is expected to become part of mainstream transportation, not only boosting traffic efficiency but also reducing the workload of drivers and lowering the incidence of traffic accidents. Plans for its widespread global adoption are already underway.
Advances and Prospects of PID Controller in Mechanical Field: From Traditional Tuning to Intelligent Optimization
Authors: Bai Zekun
Published: November 2024

Abstract. The extensive use of PID controllers in the mechanical fieldparticularly in robots and other mechanical systemsis covered in this study. In these sectors, PID controllers have become the most popular control technology due to their straightforward design and simplicity of use. PID controller performance in nonlinear complex systems is explored, along with stability and adaptive adjustment, by comparing the conventional and intelligent PID tuning methods. According to the research, control performance can be greatly enhanced by merging PID controller with intelligent technologies like fuzzy logic and genetic algorithms. These results point to the possible use of PID-based control in numerical control systems in the future. PID controllers' automatic tuning capabilities have garnered significant attention in the industrial sector. Future research will focus on the automatic tuning of PID controller to further optimize its performance and broaden its application range.
Diabetes Prediction Based on KNN, XGBoost, SVM and LR model
Authors: Yang Shu
Published: November 2024

Abstract. A. Diabetes mellitus is a chronic metabolic disease characterized by high blood sugar levels due to insulin production problems or insulin resistance. Early identification of diabetes is crucial for preventing associated complications and effectively managing the condition. This study explores the application of four machine learning models, i.e., K-Nearest Neighbor (KNN), XGBoost, Support Vector Machine (SVM) and Logistic Regression (LR) in diabetes prediction. the main goal is to assess and contrast these models' efficacy in identifying diabetes risk, thereby helping healthcare professionals make timely diagnostic and treatment decisions. The results show that the logistic regression model with an AUC value of 0.95 performs much better than the other models, demonstrating excellent sensitivity and specificity in diabetes identification. The XGBoost model also demonstrates considerable predictive accuracy with an AUC value of 0.84, highlighting its ability to effectively handle large-scale datasets. Although the SVM and KNN models had slightly lower AUC values of 0.79, they still provided reliable predictive capabilities. These results demonstrate how machine learning may be used to improve diabetes prediction.
Advancements in Diffusion Models for Image Generation: A Comparative Analysis of DDPM, LDM, and DDIM
Authors: Jin Zixiang
Published: November 2024

Abstract. This research provides a thorough exploration of diffusion models in image generation, comparing various methodologies to assess their efficacy and efficiency. The study begins with an introduction to foundational technologies and key concepts, progressing through an analysis of basic and advanced models, including Latent Diffusion Models (LDMs), Denoising Diffusion Implicit Models (DDIMs), and control models. The research evaluates these models based on their performance, computational efficiency, and future development potential. The review details the evolution of diffusion models from early stochastic processes to their current status as advanced generative models. Key principles, such as iterative noise addition and removal, are examined to understand the transformation from simple distributions to complex data representations. Innovations enhancing model efficiency, including advancements in score matching and neural network integration, are discussed. A thorough comparative analysis highlights the strengths and limitations of each model. The study identifies ongoing challenges such as interpretability and computational cost and proposes future research directions to address these issues. The findings aim to guide researchers and practitioners in advancing diffusion model technologies, offering insights into their impact on image generation and potential future developments.
Integrating Vision: From Neuroscience to Artificial Intelligence
Authors: Ye Haoshan
Published: November 2024

Abstract. This review article explores the complex interplay between neuroscience and artificial intelligence, focusing on vision processing and its applications. It starts by outlining the biological basis of vision, delving into how visual information is processed and encoded in the brain. The discussion then transitions to artificial intelligence, particularly machine vision, highlighting the advancements and technologies that mimic biological processes. A critical analysis compares how visual information is utilized in both biological organisms and artificial systems, with an emphasis on cognitive functions and neural encoding. The challenges of integrating vision across various sensory modalities are examined, underscoring the technological and cognitive limitations currently faced. The review culminates by identifying potential research paths aimed at closing the gap between neuroscience and AI. This involves enhancing the understanding and functionality of vision in multisensory contexts, striving to foster a more comprehensive approach to artificial intelligence that mirrors the complexity of human perception.
Application of Large Language Model Based on Knowledge Graph in Speech Dialogue System
Authors: Zhou Yihao
Published: November 2024

Abstract. This paper comprehensively discusses the development status and future direction of AI-driven voice interaction systems, focusing on key technologies such as automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS). With the continuous advancement of deep learning technology, voice interaction systems have achieved significant improvements in accuracy, naturalness, and user experience. However, these systems still face challenges such as accent recognition, background noise processing, complex query comprehension, and emotional expression. By analyzing the existing research results, this paper proposes that the research should focus on developing more robust models to achieve generalization across languages and dialects, and improve the system's ability to handle complex interactions.
Brain-computer interface applications in the aging population with Alzheimers and Parkinsons disease
Authors: Xiong Mengxi
Published: November 2024

Abstract. The rapidly aging population faces increasing prevalence of neurodegenerative conditions like Alzheimer's Disease (AD) and Parkinson's Disease (PD), which significantly impair cognitive and motor functions. There is an urgent need for increased research, awareness, and development of effective treatments to mitigate their impact on the affected individuals and their families. This paper delves into the history, mechanisms, and applications of brain-computer interfaces (BCIs) as innovative solutions to mitigate these impacts. BCIs enable direct brain-to-device communication, circumventing conventional neuromuscular routes. We highlight how neurofeedback and neuroplasticity facilitated by BCIs not only restore motor skills and cognitive functions but also enhance the overall quality of life for sufferers. Through applications in cognitive training, motor assistance, and advanced communication aids, BCIs present a significant promise despite the ethical and practical challenges they pose. The continual advancement of this technology promises to a future when its incorporation into treatment plans will be widely used and provide significant advantages for those with PD and AD.
Amplifier gain exploration: Technical or structural means of enhancing the gain of an amplifier
Authors: Zhang Lu
Published: November 2024

Abstract. The primary function of an amplification circuit is to amplify the input dynamic signal without distortion. Therefore, there is aproportional relationship between the output and input signals, which is referred to as gain. Gain, the ratio of output voltage to input voltage, is a crucial performance metric for amplifiers and plays a vital role in electronic circuits. Amplifiers are now an indispensable part of various fields. Amplifier circuit structures vary widely, and different amplification circuits are used in different applications and fields due to their unique advantages and inevitable drawbacks. Practically, it is necessary to maximize strengths and minimize weaknesses by sele cting the most suitable amplification circuit. This paper analyzes several typical structural methods for enhancing amplifier gain and discusses the advantages and disadvantages of each structure. Through theoretical derivation and Cadence software simulation, it is demonstrated that the cascode, five-transistor OTA structure, and cascade structure play important roles in achieving circuit gain.
A Comprehensive Analysis of Face Recognition Based on Advanced Deep Learning Technology
Authors: Xie Zilin
Published: November 2024

Abstract. Facial recognition technology is becoming increasingly important in many fields due to its non-invasiveness and rapid improvements in accuracy. This study provides a comprehensive review of face recognition methods, covering both traditional and modern approaches. The key models such as feature face, Fischer face, Visual Geometry Group face (VGG-Face), FaceNet and ArcFace are analyzed, and the complete process from data preprocessing to feature extraction and model training is described in detail. By using the Wild Labeled Face Dataset (LFW) and the Network Face Dataset of the Institute of Automation of the Chinese Academy of Sciences (CASIA-WebFace) for experiments, the research found that although the deep learning model has high accuracy, it also faces challenges such as data dependence and large computing requirements. At the same time, the research highlights the issues of fairness and bias in these models, providing directions for future research and contributing to the sustainable and ethical development of face recognition technology.
Advancements in Target Detection: A Comparative Analysis of Deep Learning Models for Real-Time Applications
Authors: Qin Tianyang
Published: November 2024

Abstract. Target detection is a vital field within computer vision, playing an essential role in applications. This paper investigates the advancements and efficiencies of contemporary target detection methodologies, focusing on deep learning frameworks. Through a systematic review and evaluation of these models' architectures and performances using the Common Objects in Context (COCO) dataset, the study highlights their operational effectiveness and practical implications in real-world scenarios. A detailed comparative analysis is conducted, assessing the models based on mean Average Precision (mAP) and input dimensions to determine their suitability across various detection tasks. You Only Look Once version 3 (YOLOv3), in particular, is recognized for its ability to combine high-speed detection with significant accuracy, effectively addressing real-time processing challenges. The results confirm that YOLOv3, despite its smaller input size, performs comparably to more complex systems, demonstrating notable enhancements in design efficiency and processing speed. This research underscores the potential for future optimizations in model architectures to bridge the gap between high-speed and high-accuracy detection tasks, potentially transforming real-time applications across multiple sectors. The practical significance of this work lies in its ability to guide future developments in object detection, benefiting both academic research and industrial applications.
Implementation of Distributed Machine Learning in Cloud Calculation: Evidence from Consumer Behavior Prediction
Authors: Ji Runze
Published: November 2024

Abstract. In the field of machine learning, distributed machine learning (DML) has gained massive popularity in research and appeared in a wide range of applications including transportation, medicine, and business. Distributed machine learning showed huge potential in processing large amounts of data while keeping the data private. In this paper, a distributed implementation of a deep neural network (DNN) with a large dataset from a telecom company, aiming to predict consumer behavior based on usage data is carried out. Various datasets with distinct heterogeneity settings were applied to the network, simulating a real-world cloud application scenario. Comparisons between both the centralized model and the distributed models were made to analyze the impact on model performance with device heterogeneity and data heterogeneity. These result indicate that under certain circumstances, DML models showed better accuracy and lower loss compared to the centralized model. The paper provided some insights and made some assumptions on how the data and the parameters affect the model performance.
Implementation of Parallel Optimization Algorithms for NLP: Mini-batch SGD, SGD with Momentum, AdaGrad Adam
Authors: Huang Wendi
Published: November 2024

Abstract. With the rapid development of machine learning technology, optimization algorithms and optimizers have become key to the development of related technologies contemporarily. Models need the help of optimizers to meet other performance indicators while saving computing resources. This research focuses on comparisons between optimizers, in the context of text sentiment classification tasks. The optimizers mainly compared in this article are mini batch SGD, momentum SGD, Adagrad and Adam. Through comparative experiments, it was found that SGD and its variants have a high dependence on the initial learning rate setting, while the performance of Adagrad and Adam is relatively balanced. Although the training time of Adagrad is shorter than that of Adam, its principal formula has flaws, which are not reflected in this task. The conclusions drawn in this article through comparison can point out the advantages and disadvantages of each optimizer, and can help realize better optimizers in subsequent research.
Face Modeling Based on Deep Learning and Traditional Methods: A Survey
Authors: Zhang Yuqian
Published: November 2024

Abstract. 3D face modeling, as an important research field in computer graphics and vision, has undergone remarkable development in recent years. The technology is widely used in various scenarios, like film and television production, virtual reality, augmented reality, facial recognition, security monitoring, and medical diagnosis. This paper aims to systematically explore the main technologies of 3D face modeling, especially the application and development of deep learning methods in it, review traditional face modeling methods, introduce in detail the latest technical advances based on deep learning, and analyze the advantages and disadvantages of these methods in practical applications.
A New Sign Language Translation System Based on Expert Model
Authors: Wu Jinbang
Published: November 2024

Abstract. The linguistic diversity of sign languages across regions presents significant challenges for the development of accurate and culturally sensitive recognition systems. To address this, This paper propose a modular sign language recognition system that detects the regional origin of a sign language and uses specialized sub-models for translation, bypassing the limitations of generalized models. This approach allows for more precise translations by catering to regional variations, ensuring higher accuracy and preserving cultural nuances.This paper also explore the creation of a standardized, diverse dataset, integrating multiple sign languages, which serves as the foundation for the systems region detection process. The dataset was meticulously annotated, normalized, and augmented to support robust model training. By adopting sub-models and utilizing region-specific data, the system improves efficiency and scalability, offering a practical solution for real-world sign language translation. This work emphasizes the importance of preserving sign language diversity while enhancing the usability and adaptability of recognition systems.
Resource allocation design method based on WSN multi-dimension soil inspection
Authors: Wang Xiaoya
Published: November 2024

Abstract. With the industrialization of agriculture and the sharp rise of food prices, in order to promote the intensive development of natural resources and achieve economies of scale agriculture in production, people put forward the innovative strategy of "precision agriculture" to maintain the sustainable development of agriculture. To protect the environment and farmer communities and increase productivity. However, in the special context of precision agriculture, the problems related to energy consumption and electromagnetic wave propagation in complex environments have a significant impact on precision agriculture. In the case of no monitoring and harsh environment, wireless sensor networks (WSN) can complete the tasks of real-time monitoring and information collection. In the past, the understanding of WSN in agriculture was limited to unilateral detection, which led to the underutilization of the soil. This paper introduces an innovative solution for precision agriculture based on wireless sensor networks, which mainly focuses on the observation and impact assessment of soil water volume,PH value, temperature and pressure in agricultural production process. It aims to reduce the waste of natural resources and the use of fertilizer substances, while maintaining or improving the yield of farming, in order to better promote the development of WSN in the agricultural field.
Revolutionizing education with AI: The adaptive cognitive enhancement model (ACEM) for personalized cognitive development
Authors: Luo Yun
Published: November 2024

Abstract. The integration of artificial intelligence (AI) into education has opened doors to personalized learning experiences. This paper introduces the Adaptive Cognitive Enhancement Model (ACEM), a cutting-edge AI-driven framework designed to personalize cognitive development for students. Leveraging advanced machine learning algorithms and quantitative analysis, ACEM adapts educational content and learning strategies to individual cognitive needs. The model encompasses five key components: cognitive profiling, adaptive learning paths, intelligent feedback, motivational strategies, and longitudinal tracking. Through quantitative analysis and mathematical modeling, the paper demonstrates how ACEM significantly enhances learning outcomes compared to traditional education models. The discussion section provides a detailed exploration of each model component, its architecture, and its role in optimizing personalized cognitive development. Furthermore, challenges such as data privacy, scalability, and model interpretability are examined, alongside potential solutions. The conclusion underscores the transformative potential of ACEM in revolutionizing education.
A review of research on training responsible large language models
Authors: Meng Yueqi
Published: November 2024

Abstract. In recent years, there has been a growing acceptance of large language models (LLM) as a mainstream method in the field of natural language processing. Consequently, numerous studies have been conducted on this topic. Training responsible Large Language Models have become a prominent subject of research in the past few years. This type of research mainly focuses on the examination of bias, morality and other aspects of LLM. There are certain similarities in the methodologies employed in those studies. This article presents a comprehensive overview of numerous recent investigations, analyzing and categorizing the methodologies employed in these studies, and offering a literature review. This review examines the three perspectives of LLM bias data set construction, bias detection and bias elimination It provides a comparative analysis of the advantages and disadvantages of different methods. After completing relevant evaluations, a comprehensive examination of the research on training responsible LLM is conducted and potential future research directions are proposed in this article.
Harnessing computational techniques to analyze and enhance fandom engagement through interaction ritual chains
Authors: Zhou Yixin
Published: November 2024

Abstract. This study explores the application of Interaction Ritual Chains (IRC) theory and computational techniques to analyze and enhance fandom engagement within the Chinese Australian community. By integrating sentiment analysis, social network analysis, and gender-based language analysis, this research provides a comprehensive understanding of how emotional energy, shared focus, and bodily synchronization contribute to fan interactions. We employ a predictive mathematical model to quantify these IRC components and their impact on engagement, leveraging data mining, natural language processing (NLP), and machine learning. The findings reveal the dynamics of fan behavior and offer insights into personalized content recommendations, gamification, and virtual reality experiences to foster deeper emotional bonds and sustained engagement. This study bridges the gap between theoretical frameworks and practical applications, providing a robust foundation for future research on digital fandoms.
Integrative approaches in cognitive neuroscience: Computational tools for analyzing EEG and fMRI data
Authors: Hu Yi , Wang Dihao
Published: November 2024

Abstract. This paper explores the integrative approaches in cognitive neuroscience that utilize computational tools to analyze electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data. These methodologies provide comprehensive insights into the complex mechanisms underlying cognitive functions. By leveraging advanced computational models, researchers can decode brain activity patterns and understand the neural correlates of cognitive processes. This paper discusses technological advancements, such as machine learning algorithms, statistical models, and signal processing techniques, and their applications in studying memory, learning, attention, perception, and decision-making. The challenges of data integration, model interpretability, and computational resources are also examined. Detailed case studies and quantitative analyses demonstrate the effectiveness of these methods in cognitive neuroscience research. The future prospects and potential improvements in this field are highlighted, emphasizing the role of interdisciplinary collaboration and technological innovation.
Applications of machine learning in quantitative trading
Authors: Gao Junbo
Published: November 2024

Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.
Comparison of Convergence for Different Models and Loss Function: Evidence from Transformer, Diffusion and RNN
Authors: Fang Xizhe
Published: November 2024

Abstract. With the development of science and technology, people are using machine learning as a method to make life easier. However, the performance corresponding to different models will produce different predictions and results for one thing. In order to improve the performance of the model, this paper investigates the influence of the learning rate hyperparameter on the model's performance, and demonstrates it through the convergence of the loss function. After experimental research, it has been found that different models exhibit significant differences in their performance when processing the same dataset. Meanwhile, different learning rates also have a significant impact on the performance of the model. Therefore, after selecting the correct model for machine learning, one should also adjust a relatively good hyperparameter to make the entire process smoother. Based on the analysis, one will gain a basic understanding of the optimal learning rates for transformer, diffusion, and RNN models when training MNIST. It is convenient for people to set better hyperparameters and obtain better prediction and decision-making results when using these three models, so that one can demonstrate better performance when using these three models.
Advancements and Challenges of Deep Learning in Facial Recognition
Authors: Li Chengkun
Published: November 2024

Abstract. Facial recognition technology allows users to achieve efficient and accurate identity verification through facial features alone, without the need to physically touch the device. Face recognition technology can be applied in a variety of scenarios such as unlocking mobile devices, online payment, attendance management and public identity verification. Nonetheless, face recognition algorithms still have some limitations in the face of massive data and complex application scenarios. Therefore, there is an urgent need for more advanced technical means to overcome the shortcomings of traditional algorithms such as low recognition rate and poor adaptability. As deep learning methods have become more popular, especially the emergence of Convolutional Neural Networks (CNN) and Transformer Networks, face recognition has been revolutionized. This paper firstly describes the principles of convolutional neural network model and Transformer model. Secondly, the applications of CNNs and Transformer networks at different stages of development in the domain of face recognition are reviewed. Then, this paper combs through the deep learning models applied to face recognition methods and evaluates their characteristics, innovativeness, usefulness and portability respectively. Finally, this paper summarizes the challenges faced by deep learning in the domain of face recognition as well as the trend of face recognition technology development in the future, aiming to point out the research focus research direction for the subsequent research.
Research on the Principles and Applications of Generative Adversarial Networks in Image Style Transfer
Authors: Qu Yubing
Published: November 2024

Abstract. With the rapid development of machine learning, the field of image style transfer has received widespread attention, and scientists have gradually realized that this is an important field in computer vision. Image style transfer refers to the conversion of an image from one visual style to another and preserving the content. The technology of changing image style has a wide range of applications in various fields from art to virtual reality. Although current learning models have achieved some results in the study of image style transfer, they still face challenges in improving resolution and complex style transfer. This paper mainly introduces the background and characteristics of existing learning models that can achieve image style transfer. It introduces the principles, characteristics and limitations of GANs. In the basis of introduction, this paper also analyses the differences and different characteristics of GANs and summarizes the advantages and disadvantages of GANs. At the same time, this paper predicts future research directions, continuing to optimize these models, reducing training costs, and exploring hybrid methods to further advance image style transfer technology. These developments not only strengthen the theoretical research of image style transfer and GANs, but also have broad application potential in fields such as art, design and medical imaging.
Gas adsorption and separation applications of MOF materials
Authors: Xu Bochun
Published: November 2024

Abstract. Low-carbon hydrocarbons (C1-C4) are really important raw materials used in making different kinds of big molecules and stuff in industries. Before, people mostly used super cold cooling and a method where stuff gets soaked up. But these ways need big machines and use a lot of energy. So, scientists are trying to make new ways to separate things that don't use as much energy. One great idea is using how different gases stick to things to separate them. This idea could be a brilliant way to replace the old methods. The most important part of this idea is making things that can stick to the gases. There's this special material called metal-organic frameworks (MOFs) that's a mix of things from nature and chemicals. It has tiny holes and places that can do special things, and it's being studied a lot for separating gases. Traditional porous materials like activated carbon, silica gel, and molecular sieves have been widely used in industries, which has greatly contributed to industrial growth and people's living standards. MOFs have changeable structures and spots that can do special things. By changing the metal bits or the organic parts, we can control the size of the holes in MOFs, the special spots, and how much surface they have. These things make MOFs useful for storing gases, separating stuff by sticking and making reactions happen in fields like chemistry [1-3]. This paper explores the categories of MOF applications.
Assessing the Impact of Anthropogenic CO2 on Marine Carbonate Chemistry: Trends, Implications, and Future Implications of Ocean Acidification
Authors: Xiang Michael S H
Published: November 2024

Abstract. This paper investigates the impact of human-caused CO2 emissions on marine carbonate chemistry, with particular attention being given to current trends and implications of ocean acidification as well as projected future projections for its effects. The study provides insight into the chemical processes underlying ocean acidification, its historical and modern progressions, as well as its biological impacts on organisms such as corals, mollusks and plankton. Furthermore, this report assesses the larger implications for marine ecosystems and fisheries as well as valuing ecosystem services that have been negatively impacted by acidification. Methodical approaches, including in situ observations, laboratory experiments and predictive modeling are discussed here to illustrate the current status of research. This paper also considers mitigation and adaptation strategies such as carbon capture and storage, enhancing ecosystem resilience enhancement measures and international policy measures as mitigation and adaptation strategies. By drawing together findings from diverse disciplines, this study seeks to identify research gaps as well as prioritize areas for further investigation, stressing the importance of joint efforts against ocean acidification.
Neural Networks for Audio Classification: Multi-scale CNN-LSTM Approach to Animal Sound Recognition
Authors: Xu Cong
Published: November 2024

Abstract. This research explores the application of neural networks, specifically CNN-LSTM models, for classifying sound signals from dogs, frogs, and cats, selected from the ESC-50 dataset. The sound data was preprocessed using Mel-frequency cepstral coefficients (MFCCs) and augmented through time stretching, pitch shifting, and noise addition to enhance model generalization in varied acoustic environments. We compared two deep learning models: a traditional CNN-LSTM and an improved version with multi-scale feature extraction, allowing for capturing both short-term and long-term sound patterns. Our findings show that the multi-scale CNN-LSTM architecture outperforms the traditional CNN-LSTM, achieving a test accuracy of 86.11% compared to 80.56%. These results highlight the effectiveness of multi-scale feature extraction for handling complex audio signals. This research offers valuable insights into bioacoustics and has broader applications in areas such as environmental sound monitoring, wildlife preservation, and animal behavior analysis.
Analysis of the Efficiency Improvements for Engines: Evidence from Jet and Marine Engines
Authors: Li Dongyu , Xu Haoling
Published: November 2024

Abstract. As a matter of fact, in recent years, the environmental issues are becoming more and more critical due to the huge pollution especially the emission and energy consumption caused by aircraft engines contributes to the problem. Thus, as two major types of engines, the jet engines and marine engines should be improved as they are the most commonly used aircraft engines. In this article, the diversity of some types of jet engines are introduced, and few modifications in the aspect of structure, power system and fuel are briefly talked about. The passage concluded some problems to overcome and actions that may be conducted in the future. At the same time, in another area that is close to jet engines, the engine design of modern large civilian ships (i.e., marine engines) mainly focusing on efficiency, environmental protection, and reliability. However, civilian ships pay special attention to reducing fuel consumption and environmental pollution, which has prompted the application of various advanced technologies.
Research on adversarial attack and defense of large language models
Authors: Yang Jidong , Chi Qiangyun , Xu Wenqiang , Yu Huaike
Published: November 2024

Abstract. Large language models (LLMs) have made excellent progress in text and image understanding and generation. However, with the wide range of applications of these models in various industries, the issue of their security, especially the defense against adversarial attacks, has become a focus of research. This study focuses on exploring the adversarial attacks faced by LLMs and their defense strategies, especially the design and optimization of defense mechanisms. Through literature review and case studies, this paper analyzes in detail the white-box and black-box attack patterns against LLMs, including model inversion, backdoor attacks, and token-based strategies. In response to these attacks, this paper proposes a series of defense strategies, including preventive measures such as data augmentation, adversarial training and model regularization, as well as real-time attack detection and response strategies such as anomaly detection and adversarial sample detection techniques. The core of this research is to improve the robustness and trustworthiness of LLMs, providing the necessary guarantees for their integration and sustainability in multiple industrial applications. In addition, this paper proposes future research directions, highlighting the importance of developing advanced defense systems, promoting interdisciplinary research and exploring new applications for LLMs. This research provides valuable insights into understanding and improving the security defense mechanisms of LLMs, which is essential for maintaining the security and user trust of these models.
Advancing language learning: The impact and challenges of Computer-Assisted Language Learning (CALL)
Authors: Luo Yun
Published: November 2024

Abstract. Computer-Assisted Language Learning (CALL) represents a transformative approach in modern education, integrating technology to enhance language acquisition effectively. This comprehensive review examines the various dimensions of CALL, focusing on its deployment through interactive software, online courses, mobile applications, and the accompanying challenges. Quantitative analyses reveal significant improvements in learner engagement and cognitive load management due to multimedia and interactive content. Moreover, the adaptability of CALL to cater to individual learning needs through personalized and adaptive learning systems demonstrates a superior proficiency acquisition rate. Despite these advancements, CALL implementation faces substantial barriers including technological limitations, the need for extensive teacher training, and concerns regarding data privacy and security. This article assesses the statistical impact of these elements on language learning outcomes and suggests strategies for overcoming the associated challenges. Through mixed-methods research and longitudinal studies, the effectiveness of CALL platforms is substantiated, highlighting their pivotal role in reshaping language education across diverse demographic and geographic spectrums.
Integration of electric vehicles in smart grids: Challenges and opportunities in achieving carbon neutrality goals
Authors: Yan Haoming
Published: November 2024

Abstract. The integration of electric vehicles (EVs) into smart grids represents a pivotal shift towards sustainable transportation, offering substantial environmental benefits and new challenges for grid stability and energy management. This paper proposes a dual-layer economic dispatch model emphasizing source-load collaboration for carbon reduction, utilizing carbon trading mechanisms and demand response strategies to enhance grid stability and carbon efficiency. The Vehicle-to-Grid (V2G) technology emerges as a key solution, enabling EVs to contribute to grid reliability and facilitating renewable energy integration. Through analysis of EV charging behavior and demand response mechanisms, the study underscores the critical role of EVs in achieving carbon neutrality goals and the necessity of innovative solutions to integrate EVs seamlessly into the grid. This research highlights the importance of collaborative efforts among policymakers, utilities, and stakeholders to address the challenges and seize the opportunities presented by EV integration, paving the way for a low-carbon energy future.
Dynamics of Sino-American relations: A decade-long analysis using topic modeling
Authors: Tang Chengyuan
Published: November 2024

Abstract. This study explores the thematic evolution of Sino-American relations over the past decade using Latent Dirichlet Allocation (LDA) topic modeling. We compiled a comprehensive dataset of academic papers, news articles, government reports, and social media posts, focusing on themes such as economic competition, technological rivalry, military strategy, diplomatic maneuvers, and ideological conflict. Our analysis identifies key sub-topics within these themes, including trade tariffs, 5G technology, and military expenditures, providing a nuanced understanding of the factors influencing bilateral relations. Temporal trends highlight the shifting prominence of these themes, with economic competition and technological rivalry becoming increasingly salient. Additionally, the concept of financial hegemony, particularly the role of the U.S. dollar, emerges as a significant driver of U.S. policy. This study offers insights into the strategic motivations behind U.S. actions and the complex interplay of various factors in Sino-American relations.