bluuebunny/crossref_metadata_2025 huggingface dataset:
Resilient Supply Chains in the Face of Disruptions: An In-Depth Study of Strategies Employed by U.S. Companies to Ensure Business Continuity
Authors:   University of Pennsylvania - The Wharton School, Silverman Julia J.
Published: October 2023

This study retrospectively explores the strategies employed by U.S. companies to ensure business continuity in the face of disruptions within their supply chains. The purpose of this research was to investigate the effectiveness of various resilience strategies adopted by American businesses and to derive insights that can inform future supply chain management practices. The problem addressed in this study was the increasing vulnerability of supply chains to disruptions, including natural disasters, global pandemics, and geopolitical tensions. These disruptions could result in significant economic losses and operational challenges for businesses. Understanding how U.S. companies tackled these challenges in the past provides valuable lessons for enhancing supply chain resilience. To achieve these objectives, a mixed-methods research approach was employed. Quantitative data were collected through surveys distributed to a diverse sample of U.S. companies across different industries. Qualitative data were obtained through in-depth interviews with supply chain and operations managers from a subset of these companies. The study analyzed historical cases of supply chain disruptions and the corresponding strategies used to mitigate their impact. Findings from this research indicate that U.S. companies employed a combination of strategies to enhance supply chain resilience, including diversifying suppliers, leveraging technology for real-time visibility, and implementing risk management frameworks. Companies that had robust business continuity plans in place were better equipped to respond to disruptions effectively. In conclusion, this study highlights the critical importance of proactive supply chain resilience strategies in mitigating the impact of disruptions. The lessons learned from past disruptions provide valuable insights for companies seeking to build more resilient supply chains in the future. Recommendations include the development and regular testing of robust business continuity plans, the adoption of digital technologies for enhanced visibility, and a proactive approach to risk management. By drawing from past experiences, U.S. companies can better prepare for and respond to future disruptions, ultimately ensuring the continuity of their operations and safeguarding their competitiveness in an ever-changing business landscape. Keywords: Resilient Supply Chains, Disruptions, U.S. Companies, Business Continuity, Strategies
Resistance, Performativity, and Fragmentation: The Relational Arena of EDI/D in Canadian Higher Education
Authors: Marom Lilach
Published: October 2023

Equity, diversity, inclusion, and decolonizing (EDI/D) have gained discursive centrality in Canadian higher education (HE) yet are criticized as performative. Donald’s work on “ethical relationality” understands colonialism as a denial of relations. Drawing on this work I analyze EDI/D in HE through a lens of relationship building. The study maps three institutional layers of EDI/D. The external layer is centred on confronting overt critique, with discourses about EDI/D as threatening academic freedom becoming more prevalent. The second layer focuses on the “mainstream” adoption of EDI/D. EDI/D became central in terminology, but HE culture is more resistant to change. The inner layer includes those engaged with EDI/D. It is grounded in collaboration, but also in containment and fragmentation of EDI/D initiatives. These layers reveal institutional gaslighting tactics that derail meaningful engagement with EDI/D. For EDI/D to be transformative, HE institutions must ground EDI/D in the difficult process of relationship building.
Gait analysis of biped robot based on inverted pendulum model
Authors: Wan Zichen
Published: October 2023

The spring inverted pendulum model was used to analyze the motion of the biped robots under different motion states and the effective model parameters were obtained. In this paper, the biped robot was simplified to an inverted pendulum model and the kinematics equations of the robot under different motion states were obtained. The kinematics equations were solved by MATLAB, and the analysis was used to get the right sports condition during motion process. The corresponding simulation of the inverted pendulum model was carried out based on MATLAB, a series of data such as the trajectory of mass point of the robot and the variation of the ground reaction force during motion process were obtained. By analyzing the results, the model parameters and conditions of the biped robots to keep stable in different motion states were obtained.
An overview of cloud storage data auditing schemes
Authors: Cui Dongchen
Published: October 2023

With the rapid growth of the internet today, cloud storage services have had an impact on numerous internet users worldwide. Internet users, in order to avoid potential risk of data loss, can outsource their local data to remote cloud servers instead of using local media. However, this kind of cloud storage services is sometimes unreliable enough and the data security of users cannot be guaranteed. In this case, data auditing mechanisms for cloud storage are studied to avoid the destruction of user data. The traditional audit schemes are based on provable data possession mechanisms, while the third-party organization relies on the recent blockchain-based methods, conventional single-cloud storage system and recently emerging multi-cloud storage models. Some audit schemes not only provide auditing capability, but also have some other ancillary functions to ensure applicability for different potential malevolent situations. Gathered correlated studies about data auditing schemes of cloud service are analyzed and classified in this paper, based on research objects and research methods in order to seek probable innovation points. We also analyze experiments in their papers. Toward the finish of this review, we sum up all the examination, list the bearings not investigated, and give a few creative spots.
A corpus study on the relationship between focus and accent in natural speech in Mandarin Chinese
Authors: Dong Ruiting
Published: October 2023

This paper aims to explore the relationship between focus and accent, namely, whether the focus of a sentence possesses some acoustic features of accent. Previous studies suggest that focused words depict different patterns of acoustic prominence embedded in accent. According to previous evidence found in English, pitch and duration are two crucial factors contributing to differentiating focused and unfocused words. Therefore, pitch and duration are the main two factor considered in this study. Studies in aspect of the relationship between focus and accent and their realization in the phonetic level in natural speech are insufficient. Therefore, this study aims to provide support for the connection between focus and accent in natural speech in Mandarin. Based on 24 sentences from three native Mandarin speakers selected from a Chinese corpus, THCHS-30, published by Tsinghua University, this study extracts the narrow-focused words, broad-focused words, and the corresponding unfocused-words as the main subjects and analyses their mutual relationships. Results support the symmetric relationship between focus and accent. Focused words show phonetic features of accent and words with no obvious phonetic prominence of accent are unlikely to be considered as focus. Focus triggers a decrease in F0 values and an increase in duration of focused words. Linear mixed effects tests in R suggest that a major effect of narrow and broad focus in causing F0 and duration changes has not been deducted, but narrow focus conditions can result in duration proportion increase of narrow focused words compared to broad focused ones.
The application of Long Short-Term Memory algorithm in American multinational technology company stock prediction
Authors: Wang Yufei
Published: October 2023

The fast development in American multinational technology companies has attracted both professional and new investors to buy the stocks. However, the price of these companies are unstable and therefore hard to be predicted. The focus of this article is to use AI and deep learning algorithms to find a pattern of the stock price. Long Short-Term Memory Algorithm (LSTM) is the main algorithm used to predict the trend, and other methods including Autoregressive integrated moving average (ARIMA), Seasonal autoregressive integrated moving average (SARIMA), and prophet are also discussed in this piece.
3D point cloud domain generalization via adversarial training
Authors: Sun Yichen
Published: October 2023

The purpose of the paper is to tackle the classification problem of 3D point cloud data in domain generalization: how to develop a generalized feature representation for an unseen target domain by utilizing sub-field of numerous seen source domain(s). We present a novel methodology based on both adversarial training to learn a generalized feature representations across subdomains in domain adaptation called 3D-AA. We specifically expand adversarial autoencoders by applying the Maximum Mean Discrepancy (MMD) measure to align the distributions across several subdomains, and then matching the aligned distribution to any given prior distribution via adversarial feature learning. In this manner, the learned 3D feature representation is supposed to be universal to the observed source domains due to the MMD regularization and is expected to generalize well on the target domain due to the addition of the prior distribution. We applied an algorithm to train two different 3D point cloud source domains with our framework. The combination of multiple loss functions on 3D point cloud domain generalization task show that our applied algorithm performs better and learn more generalized features for the target domain than the source-only algorithm which only utilized the MMD measurement.
Analysis of microblog users influence based on the social network model
Authors: Yiyang Xiang
Published: October 2023

With the development of science and technology in today's world, social software and networks have become more and more advanced. Microblog is a widely used application and everyone in China has to contact it. According to calculations, thousands of microblogs can be generated every second, so the impact of microblogs is huge. At present, the research on microblogging social networks is relatively few, and the research on user influence in microblogging is even less. Even if the influence of users in microblogs is studied, the methods used are relatively complex, and it is impossible to simply and clearly select users with greater influence. Therefore, based on the social network model, this paper first uses a Web crawler to obtain microblog data. This paper obtains only one user's id, gender, age, city, number of followers, number of posts, attitudes, and comments. When this paper gets the data, this paper uses the knowledge of graph theory to simplify the specific data, so as to better study the relationship between the data and explore their influence. This paper regards every user in microblog as a node, and the relationship between the users is the edges. Then calculate each nodes Degree Centrality and Eigenvector Centrality to find influential users. This paper finds that the influence gap between different users is large. Women's influence is slightly higher than men's. The youth group is significantly larger than that of the others. The influence of first tier cities and new first tier cities is slightly higher than those of second tier cities, and their influence is significantly higher than those of other cities. These results will help people suit the remedy to the case and achieve better communication results.
A novel multivariate approach for COVID-19 pandemic prediction in the United States
Authors: Yu Han
Published: October 2023

Since December 2019, the COVID-19 pandemic has caused enormous economic and social disorder. The analysis and forecasts of pandemic has received considerable attention worldwide. Previous research has primarily focused on predictions based merely on historical data and thus has been unable to identify the effect of external factors such as government policy responses. This study aims to develop a multivariate VECM model to predict the change in confirmed cases with Government Stringency Index taken into consideration. This study carried out exponential smoothing and VECM cointegration test using COVID-19 related data of the United States. By exploring the results of the two forecasters, it becomes evident that VECM has shown a much higher accuracy than Exponential Smoothing in the long run. In particular, when huge changes of tendencies in confirmed cases occur, the improvement appears to be more obvious.
PG-Net:3D point cloud completion based on graph convolutional network
Authors: Wang Tiansheng
Published: October 2023

With the advancement of autonomous driving technology, the problem of 3D point cloud completion has become increasingly important. Completing 3D point clouds can improve the accuracy of 3D object detection, which is crucial for the development of autonomous driving and other related fields. In this paper, we propose a new approach for 3D point cloud completion tasks using point cloud representation. We focuses on the point cloud completion problem using Graph Neural Network methods, which are known for their ability to capture topological features. Our approach utilizes key components extraction and learning from the point cloud, to constrain the output of the decoder, and thus enhance the performance of point cloud completion task. Our approach is able to overcome the limitations of traditional methods, such as memory consumption and computational burden, as well as the loss of detailed information caused by quantization operation in some sparse representation based methods. We conduct extensive experiments on several benchmark datasets to evaluate the performance of our approach and compare it to existing methods. Our experimental results demonstrate that our proposed method is competitive, achieving comparable or even better results compared to state-of-the-art models. In particular, we show that our method is able to improve upon the performance of earlier models and achieve results that are comparable to current state-of-the-art models. These results indicate that our approach is a promising solution for 3D point cloud completion tasks.
Research on transformer and attention in applied algorithms
Authors: Zhang Junyan
Published: October 2023

The transformer is an encoder-decoder-based structure and model for deep learning that completely utilizes the self-attention mechanism. It has gained remarkable success in natural language processing and computer vision and is becoming the predominant research direction. This study first analyzes the transformer and attention mechanism, summarizes their advantages, and explores how they help the recommendation algorithm dynamically focus on specific parts of the input that are helpful to perform the current recommendation task. After analyzing the framework of the attention mechanism network and its weight computation for data received. To further enhance the practicality of objects in natural situations and the precision of object recognition, a transformer detection approach based on deformable convolution is presented. And analyzed how the transformer works in the generative pre-trained transformer. These algorithms illustrate the efficacy and robustness of the transformer, indicating that the transformer that incorporates the attention mechanism may satisfy the requirements of the majority of deep learning tasks. However, the unpredictability of demands, the exponential growth of information, and other issues will continue to make it challenging to deal with global interaction mechanisms and a unified framework for multimodal data.
Computational Methods for Inferring and Reconstructing Gene Regulatory Networks
Authors: Zhou Xianglu , Jia Haiyang
Published: October 2023

Gene regulatory network (GRN) describes dynamic and complex gene interactions that determine the functions of cells. Ergo, understanding of GRNs has played a great role in cancer treatment, drug design, and gene research. However, the GRN study suffers from a lack of experiment measurement data and the complexity of the model. Computational approaches have evolved in recent years to promote the study of GRN. In this review, we summarized popular Computational approaches for GRN reconstruction. We started with traditional bioinformatic methods, such as Bayesian networks and mutual information methods. Later, we introduced how today's hot technology in the computer field - machine learning benefited GRN research. Tree-based approaches and other machine-learning methods are elaborated on in this section. We discussed not only the advantages and progression brought by various methods but also the drawbacks and limitations, such as the accuracy and robustness of GRN reconstruction. We expect to inspire readers for improved GRN study approaches via our introduction to this field.
Research on the improvement method of CAN bus based on CAN-FD
Authors: Liu Jie
Published: October 2023

CAN bus fault diagnosis of new energy vehicles is the main factor affecting the maintenance efficiency of new energy vehicles, so a convenient and fast diagnosis method is particularly important. The CAN bus fault diagnosis of new energy vehicles has gradually become an important factor affecting the promotion of new energy vehicles in the post-market. CAN plays a crucial role in industrial application especially in automotive industry. For adapting the Big Bang of data, CAN need to be improved. CAN FD is a protocol which is raised to solve some shortages in CAN.This paper focus the way how to improve the CAN based on CAN-FD.By contrasting these two communication protocols ,this paper analyzes the way to improve CAN in four aspects which is include physical layer,the length of data frame, the rate of the data transmission and the way about error validation. In conclusion, the improvement of CAN, not only the rate and the data size in data transmission should be stipulated, also make some change in many ways to adapt the requirement so that the data can be transmitted rapidly and efficiently.
Related methods introduction and application analysis of I2C bus
Authors: Wang Zicheng , Weng Yifan , Wu Qinyu
Published: October 2023

Major changes have taken place in the global integrated circuit industry, and various emerging technologies have promoted the development of chips in the direction of smaller size and larger scale. Verification is the key line of defense to ensure the correct realization of chip functions. Verification also takes up a lot of time in the actual chip project cycle. Improving verification efficiency is of great significance to reducing chip costs. Universal Serial Bus IIC (Inter-Integrated Circuit) has the simple structure of two-way and double-wire, unique data transmission mode and its wide use in system-on-chip. This paper presents a design and implementation of IIC protocol based on Verilog language using Modelsim simulation software. The IIC protocol is a serial communication protocol used for data transfer between electronic devices. The paper provides a detailed introduction to the principles, communication process, and data format of the IIC protocol, and proposes a Verilog-based design solution. Through Modelsim simulation, the correctness and reliability of the design are verified
Research and analysis of matrix multiplica-tion in distributed learning algorithms
Authors: Zhou Xuze
Published: October 2023

Matrix multiplication have become increasingly important nowa-days, which is applied in many kinds of fields. In this case, the need to improve the speed and efficiency of matrix multiplication is in-creasing. In this paper, the author analyzes some relevant theories about matrix multiplication as well as the advantage and disad-vantage of some applications that based on matrix multiplication. It turns out that matrix multiplication has a lot of room for devel-opment in the future cause the current method still has many de-fects and is not perfect. For example, it will still take plenty of time to finish the process of matrix multiplication when the matrices are large in size. However, its gratifying that some improvements have been achieved, which can help to optimize the efficiency. In addi-tion, some advanced methods start to appear. For instance, the method of combining matrix multiplication with AI provides a new direction for future research and development. Consequently, it is predictable that a significant achievement to optimize matrix mul-tiplication will be made in the future.
Analysis and application of key technologies in digital signal processing
Authors: Wang Ziyu
Published: October 2023

With the uncreasing reform of times and the continuous advancement of my science and technology, in recent years, my overall national strength is strengthening gradually. I have entered an information age, one digital age, the use of digital signal processing is becoming more and more frequent and important. Therefore, this paper mainly analyzes the application of DSP, the digital signal processing technology show to make the exposition. Digitalization is a major trend in today's society and the main theme of the development of the times, while digital signal processor is a component born with the times. The so-called digital signal processing is to use a universal digital signal chip to extract useful information from the signal by digital calculation. In recent years, in the wake of the constantly improving of science and technology and the continuous reform and innovation of technology in China, DSP is more and more close to people's production and life, and plays an important role in practical applications. Therefore, it is necessary to discuss and analyze its development.
Control method and analysis of potential application based on memristor
Authors: Gu Ziyan
Published: October 2023

Memristors have many advantages, such as small size, fast speed, and low power consumption. If the cross array is used as the memristor, a structure similar to the matrix can be obtained, which can store data and do calculation. Based on the characteristics of memristor, the realization of various control functions has a wide range of application prospects and advantages. The memristor matrix supply can be used to achieve relevant control, so that the control becomes simple and efficient. At the same time, memristor has outstanding advantages in arithmetic calculation, neural network, and artificial intelligence. This article introduces memristor and shows the theory needed to apply memristor to the control of robot arm, improve MAC calculation by memristor and the application in neural network. There is no doubt that the research in this paper will contribute to the further exploration of the control method of memristor and lay a solid foundation for its practical potential application theory in the future.
Research on distributed coding computation based on matrix multiplication
Authors: Zhang Lin
Published: October 2023

Matrix multiplication is used in machine learning to train larger, more accurate models with massive data more effectively. The distributed machine learning paradigm necessitates the distributed computing of many matrix multiplications once it is adopted. The execution time of distributed machine learning algorithms increases on dropout nodes, which are computational nodes in distributed clusters that randomly slow down computation due to a variety of factors (e.g., node failure, system failure, communication bottlenecks, etc.), becoming a significant bottleneck in distributed computing systems. It has been discovered that coded computation is less expensive than replica methods and can more effectively reduce the effects of dropped nodes. In this study, we present theoretical insights on how encoded solutions might produce significant gains over unencoded solutions.
Design and implementation of UART receiving module based on FPGA
Authors: Zhang Xiaowen
Published: October 2023

In modern electronic systems, data transmission is an essential requirement within and between boards, or between lower and upper computers. To ensure data transmission accuracy, communication protocols are established that must be followed by all parties involved. These protocols include UART (universal asynchronous transmitter and receiver), IIC (Inter-Integrated Circuit), SPI (Serial Peripheral Interface), USB2.0/3.0(Universal Serial Bus), and Ethernet. Among these protocols, UART is the most basic one and is widely used in embedded devices due to its simple circuit structure and low cost. With the exponential growth of information technology, UART-based embedded devices can easily achieve wired and wireless communication through various communication interfaces and wireless modules. In this paper, the author presents an example of a receiving module for UART communication that converts parallel data into string data. The entire module is developed using the hardware description language Verilog HDL. Simulations are performed using ModelSim, and the results demonstrate that the simulation waveform is consistent with the expected receiving data. This approach facilitates the transformation of serial data to parallel data, improving the efficiency and accuracy of data transmission.
Research and analysis of FIFO related working principles
Authors: Gao Yiting
Published: October 2023

An asynchronous FIFO is a classic circuit device used for caching data and accommodating the frequency of asynchronous signals. With the continuous improvement of technology, the advantages of this device in real-time transmission, multi-core processing, and data acquisition have become more apparent. As a result, the original structure has been optimized and extended using algorithm and design techniques to achieve greater efficiency and applicability in various academic and industrial applications. This article discusses several methods for optimizing FIFO in different aspects and examines how an unlocked FIFO queue is applied in a CAN bus data acquisition system, as well as its use in data hybrid framing technology. The findings show that FIFO performs exceptionally well as a cache structure, particularly in situations involving large amounts of data acquisition and real-time procession. The various optimization techniques discussed in this article include using pipeline registers, implementing a dual-clock FIFO, and employing Gray-code-based pointers. These techniques can increase the speed and reduce the power consumption of FIFO, thereby improving its overall efficiency. In addition, the article introduces the unlocked FIFO queue, which provides more flexibility than conventional locked FIFOs. Unlocked FIFO queues allow multiple cores to access the buffer simultaneously, which makes them ideal for high-performance systems. Finally, the article explores the application of FIFO in CAN bus data acquisition systems, where it is utilized as a buffer between the bus and other devices. Additionally, the use of FIFO in data hybrid framing technology is discussed, where it is essential for maintaining the integrity of the data stream.
A guide to memory and virtual reality recall
Authors: Hou Yinzuo
Published: October 2023

Background: As time passes, people forget the past and everything that happened in the past fades into memory with the passage of time. Time, space and orientation are added. Objective: To recreate scenes, evoke memories and share memories through the adaptation of virtual reality technology. And, allowing for a more realistic reproduction of memories. Methodology: Use software to input text, video, audio, photos, etc., set the scene and recreate it through potential logical substitution based on the five senses (hearing, sight, touch, taste, smell). This work is not only an effective aid to the treatment of mental illnesses. Another example is the cultural heritage, which is a very effective solution to the problem of passing on a particular technique. Also, it is a new way for people to record and share their lives, to explore the past and to preserve the good things. My point in designing this project is that through this project memories can be preserved more completely and the past can be more authentically recreated.
App design based on the protection of personal privacy information in China
Authors: Qin Xiangru
Published: October 2023

In today's world, digital technology is rapidly evolving and the way people live their lives is changing dramatically. For example, tools such as computers, the internet and mobile phones are bringing us new experiences like never before, while digital technology is also bringing unprecedented advances and innovations to business and science. New opportunities are growing and traditional hierarchies are beginning to fall apart - and with them comes a breakdown in trust. Google, Instagram, Wechat, Xiaohongshu - these disruptive platforms,networks and technologies have changed the current status of their respective industries, making our lives easier while there are bad actors in the unseen grey areas stealing and exploiting our privacy and data. The emergence of COVID-19 in recent years has forced us to use and rely on digital devices with high frequency, especially in China, which has accelerated our transition to a digital world, with both advantages and disadvantages. This article will explore how to better protect the privacy and data of digital nomads within reasonable and lawful limits, and provide an idea as a solution. The article presents the idea of a data protection app called Data Butler, which is an application dedicated to protecting the privacy of its users. The aim is to enable users to enjoy the benefits of big data while protecting their private data. Data Butler is designed to enable users to enjoy the benefits of big data while their private data is protected.
Breast cancer risk prediction leveraging K-nearest neighbor and logistic regression algorithms
Authors: Han Shanxiang , Zeng Xincheng , Zhou Chunlin
Published: October 2023

Based on the latest global cancer data collected by IARC, it is estimated that 19.293 million novel cancer cases will be diagnosed in 2020 worldwide. Among them, there will be 2.261 million novel patients suffering from breast cancer, which occupies 11.7% of the entire number of novel cancer cases worldwide and 24.5% of the total number of novel cancers in women. Moreover, there will be 685,000 deaths of breast cancer in women worldwide in the same period, ranking first in the incidence and death of cancer in women. In this paper, the authors compare two classical algorithms, KNN and logistic regression, predicting the risk of breast cancer. The dataset includes medical records with various attributes. The study found that both algorithms achieved high accuracy in predicting breast cancer, with KNN achieving an accuracy rate of 97.5% and logistic regression achieving an accuracy rate of 95%. The study also found that age, tumor size, and lymph node status were the most important predictors of breast cancer for both algorithms. Overall, the study demonstrates the effectiveness of KNN and logistic regression algorithms in predicting breast cancer and provides valuable insights into the most important predictors of the disease. The findings can potentially contribute to the development of more accurate and efficient diagnostic tools for breast cancer diagnosis.
An empirical comparison of machine learning methods for predicting breast cancer probability
Authors: Ge Wenqiang
Published: October 2023

Nowadays, there are many kinds of cancer that people cannot prevent early, so those diseases cannot be controlled effectively when people detect them. To solve the problem, more and more scientists started to study the features and possible reasons for those diseases. This study concentrates on the relationship between some basic physical features, breast cancer diagnosis, and patients' possibilities of getting breast cancer. The main methods in this research are machine learning. It includes logistic regression, decision tree, random forest, k-nearest neighbor, and gradient boosting model. These methods use their main ideas to construct and fit the training data, then they predict the testing data and compare them with the testing set. Each model has its prediction results and performance values. After that, they will also herald the patients' probability of getting breast cancer with their physical conditions. According to the evaluation values, the random forest model performed best. Regarding probability prediction, the models have almost similar results. This study is a simple attempt to study the possible reasons for breast cancer. People could concentrate on increasing the number of independent variables in future studies. For example, they can add potential triggers of breast cancer. This suggestion will improve the efficiency of disease prevention and explore some helpful treatments.
Recognizing facial emotion leveraging convolutional neural network
Authors: Yang Zhike , Zhang Zhengqian , Zheng Bowen
Published: October 2023

Facial expression recognition technology is a technology based on the combination of artificial intelligence technology and biology, and it is interdisciplinary research. The appearance of this technology shows the diversity of the development direction and application fields of computer technology, but it also means that the development of facial expression recognition technology needs not only the support of computer technology but also the exploration and progress of biology. At present, to recognize facial expressions a system usually contains three stages, namely, face detection stage, feature extraction stage, and face expression recognition stage. The facial expression recognition algorithm is transformed into a classification problem, and a convolutional neural network (CNN) is adopted, where features of the facial expressions could be automatically extracted. It takes as input a preprocessed image and can directly produce encoded features and predictions, leveraging the end-to-end strategy. It could discard the complicated intermediate modeling process of traditional machine learning. In this work a CNN is implemented for the recognition of facial expression on Fer2013 dataset. Moreover, the effectiveness of different numbers of CNN layers are testified on this problem. It could be concluded that with deeper architectures, the CNN tends to perform better.
A research on applications of neural network-based cryptography
Authors: Liang Yuheng
Published: October 2023

With the rapid development of neural networks, they have been integrated into more and more fields, and cryptography is no exception. Research on the combination of neural networks and cryptography is developing rapidly, and numerous scholars have made significant progress in these fields. Neural network-based cryptography is a complex system that deserves an in-depth study. This paper will first introduce the foundation types of neural networks, some of the other types of neural network , and cryptographic techniques. Immediately after, this paper will introduce the scheme of Generative Adversarial Neural Networks (GANs) generating cryptography and the scheme of combining GANs and Convolutional Neural Networks (CNNs) generating cryptography and analyze their differences and advantage and disadvantages. This paper will then introduce the application of neural networks in blockchain, key exchange technology, and the combination of neural networks and Advanced Encryption Standard (AES). As well as at the end of this paper will show some views of combining neural networks and cryptography in the future.
Research and application of deep learning in Metaverse
Authors: Ren Keting
Published: October 2023

The Metaverse, an immersive and interactive virtual environment, has garnered significant attention and presents numerous opportunities for technological advancements. The incorporation of artificial intelligence (AI) within the Metaverse can lead to transformative changes across various industries and applications. This paper delves into AIs role in the Metaverse, specifically focusing on deep learning and reinforcement learning techniques to address challenges faced by different applications with in- sufficient data. An overview of AI within the Metaverse is provided, along with an exploration of the connection between the Metaverse and the Digital Twin concept. The establishment of the Metaverse environment and AI- driven activities are examined, emphasizing their applications in virtual environments and interactions. Moreover, innovative deep learning training directions leveraging the Metaverse for various applications are proposed, including autonomous vehicles, surgical medical robots, nuclear decommissioning robots, and industrial design and experimentation. Finally, the paper summarizes and looks forward to the full text.
Cat classification based on improved ResNet50
Authors: Sun Shipeng
Published: October 2023

Cat species recognition holds significant potential in many fields. The primary objective of this research is to develop an automated algorithm for recognizing the presence of cats in images. The application prospects of this algorithm are diverse and include security, image search, and social media. Hence, this research has considerable practical value in various domains. In this study, we propose a cat image recognition algorithm based on the PyTorch, with ResNet50 as the foundational network architecture, and an attention mechanism (Efficient Channel Attention) integrated into the model for improved performance. We first introduced the Resnet network, and then introduced the combination of attention mechanism and Resnet in detail The proposed model achieved a 92.37% accuracy rate in classifying the 12 cat species, demonstrating its efficacy in accurately classifying and recognizing the collected images. The research conclusion of this paper has certain reference value.
Balance of rights in the protection of users' data interests in the era of big data
Authors: Cong Chuanxiang
Published: October 2023

The protection of user data interests is of utmost significance in this day and age, when everyone's private life details and possessions are being digitised and stored in the cloud. Data now rules the world. This paper reviews the relevant literature in order to study the balance of rights in the protection of user data interests in the era of big data. It then suggests various countermeasures for the problem that it identifies. According to the findings of this study, the key factors contributing to the imbalance in the protection of user data rights are the progression of science and technology, the increase in the value of user data, and a lack of awareness regarding the protection of user personal data rights.
Comparison and analysis of DQN performance with different hyperparameters
Authors: Huang Zhuoxian , Ou Jiayi , Wang Ming
Published: October 2023

Deep Q-learning Network (DQN) is an algorithm that combines Q-learning and deep neural network, its model can adopt high-dimensional input and low-dimensional output. As a deep reinforcement learning algorithm proposed ten years ago, its performance on some Atari games has surpassed all previous algorithms, even some human experts, which fully reflects DQNs high research value. The tuning of hyperparameters is crucial for any algorithm, especially for those with strong performance. The same algorithm can produce completely different results when using different sets of hyperparameters, and suitable values can considerably improve the algorithm. Based on the DQN we implement, we test on number of episodes, size of replay buffer, gamma, learning rate and batch size with different values. In each round of experiments, except for the target hyperparameter, all others use default values, and we recorded the impact of these changes on training performance. The result indicates that as the number of episodes continues to increase, the performance improves steadily and degressively. The same conclusion is also applicable to the size of replay buffer, while other hyperparameters need to be given values to have optimal performance.
Research on panoramic mosaic of multiple images
Authors: Liu Haoxuan
Published: October 2023

With the continuous improvement of the manufacturing process of mobile phone cameras, the demand for panoramic photos by the industry and the public is not limited to ordinary cylindrical panoramic images, so a series of accurate panoramic image stitching technologies have been derived. However, these techniques often fail to take into account the stitching effect and speed, and there is still a technical gap in current multi-image panoramic stitching techniques. This article mainly compares the accuracy and efficiency of various feature extraction algorithms to select the most suitable algorithm for obtaining feature point pairs and estimating the homography matrix. Finally, a dual band hybrid algorithm is used to distort and fuse multiple images into panoramic photos. The results show that the panoramic images generated with multiple images using the algorithm provided in this paper are very good, even though there is a little bit of light and dark interlacing and ghosting.
Research on digital currency based on encryption technology
Authors: Zhang Yubo
Published: October 2023

Digital currencies have become an increasingly popular topic of discussion in recent years. Digital currencies are virtual forms of currency that operate outside the traditional banking system. They are based on cryptographic technologies and are often decentralized, meaning they are not controlled by a central authority. The most well-known digital currency is Bitcoin, but there are many other types of digital currencies in existence. Digital currencies can be used to purchase goods and services online or transferred between users directly without intermediaries like banks. They have gained popularity due to their potential for increased security, transparency, and efficiency in financial transactions. In today's digital currency, a variety of digital currencies emerge in an endless stream, and crypto technology is also constantly developing to improve the security of digital currency payments. In section 2, this paper briefly introduces several common digital currencies and encryption algorithms, and in section 3, this paper introduces these typical digital currencies in detail through the analysis of representative literature. Bitcoin is mainly encrypted based on blockchain technology, and its encryption principle is mainly divided into three parts: public key encryption, hash function, and proof of work. Ethereum is a distributed blockchain platform with encryption principles similar to Bitcoin, including public key encryption and hashing algorithms. Ripple is a distributed cryptocurrency. Its encryption principle mainly adopts the public-private key encryption system. In terms of encryption technology, blockchain technology, the Hash algorithm and symmetric and asymmetric encryption are also popular encryption algorithms in digital currencies.
Research on accuracy analysis and improvement of recommender system based on amazon review
Authors: Wang Xuyang
Published: October 2023

Recommender system is a system that uses artificial intelligence and data mining technology to recommend items or services that meet users' preferences based on their historical behaviors and interests. In modern society, people are faced with more and more choices, and the emergence of recommender system can help users filter out valuable content from complex information and improve user satisfaction and experience. In this paper, collaborative filtering is used to implement a recommender system based on Amazon review data set. Meanwhile, Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are used to conduct dimensionality reduction and other operations on the data. Root Mean Squared Error (RMSE) A value expressed as a recommendation for accuracy. After the establishment of the recommender system and three precision analysis experiments, it achieves this by applying a selected filtering algorithm to the input supplied, which is frequently in the form of user reviews of products.
A research on quantum digital signatures
Authors: Duan Haoxuan
Published: October 2023

This paper provides an overview of the basic principles, types, recent works, and applications of quantum digital signatures. The security of traditional digital signature schemes is compromised by the rise of quantum computing, leading to a need for post-quantum cryptography. Quantum digital signatures, which rely on the principles of quantum mechanics, offer a potential solution to this problem. This paper aims to provide a comprehensive overview of quantum digital signatures and post-quantum digital signatures. The paper introduces the basic principles of quantum mechanics, then explains key distribution in quantum digital signatures. The paper then provides a detailed description of both quantum and post-quantum digital signatures, including their differences and applications. Finally, the paper summarizes the main findings in the field, highlights potential future directions, and discusses challenges that humans must address. In addition, the paper examines the widespread applications of quantum digital signatures and post-quantum digital signatures in various fields such as Bitcoin, smart city blockchain, and finance. Finally, the paper summarizes the key findings in the field, highlighting potential future directions and discussing challenges that humans must address. Overall, this paper aims to provide readers with a comprehensive understanding of quantum digital signatures and post-quantum digital signatures and their applications in various domains.
A new interpolation algorithm based on Hibbard-Laroche algorithm and its superiority
Authors: Huang Yuxuan , Ren Yiming
Published: October 2023

In order to optimize the possible problems and improvements in the existing color image restoration interpolation algorithms, we conduct research based on the existing bilinear interpolation method, cok algorithm and Hibbard-Laroche algorithm. Our method is to use our own comparison method to compare different types of images through three algorithms to find the advantages and disadvantages and to some extent combine the advantages of bilinear interpolation and Hibbard-Laroche algorithm to try to innovate a new algorithm to compare with the existing three algorithms. The results show that the existing three algorithms have their own advantages in different scenarios, and the new algorithm is superior to the existing algorithms in terms of clarity and color restoration accuracy in most scenarios. However, due to the large computational complexity, the operation speed is slow.
Research on pathologic myopia recognition based on vision transformer
Authors: Yang Chen
Published: October 2023

Currently, the diagnosis of pathological myopia is mostly done through manual diagnosis, which not only requires experienced ophthalmologists but is also time-consuming and labour-intensive. In order to improve the diagnostic efficiency and accuracy, and to prevent irreversible visual impairment caused by missed diagnosis, misdiagnosis, and delayed treatment, this paper presents a fine-grained image analysis task of classifying fundus images of patients with pathological myopia and non-pathological myopia. To accurately identify subtle differences in features among similar fundus images, a pathological myopia recognition model based on Vision Transformer (ViT) is proposed. The model incorporates a feature selection module using self-attention mechanism that can effectively select important features in the fundus images, thereby eliminating the influence of irrelevant regions on recognition. Experimental results demonstrate that this method outperforms traditional ViT models, achieving high accuracy in pathological myopia recognition.