bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study of Equity Incentive Schemes in Technology-based Companies: The Case of iFLYTEK
Authors: Bi Qian
Published: April 2023

In modern companies, equity incentives have been widely used to improve corporate governance. However, not all equity incentives can achieve the desired effect of the managers, and if the internal factors of the equity incentive scheme are not set properly, the ultimate benefit is often minimal or even negative. As a technology company, iFLYTEK has implemented a number of equity incentive schemes in recent years, but not all of them have achieved satisfactory results. The equity incentive schemes adopted by iFLYTEK have both common typical problems and problems unique to the industry. This paper will take the equity incentive scheme of iFLYTEK as the research object, analyze its scheme, subsequent implementation status and problems, and make suggestions for equity incentive schemes for technology companies.
A Novel Risk Control Method of Supply Chain Finance for Commercial Banks with Big Data
Authors: Wang Han
Published: April 2023

Commercial bank supply chain financial business is mainly divided into accounts receivable financing mode, inventory financing mode and advance financing mode. Traditional supply chain financial business of commercial banks in different modes will form different risks, including small and medium-sized enterprise credit risk, the core enterprise credit risk and credit risk of the third-party logistics warehousing company. Under the background of increasingly powerful financial science and technology, we can use more financial technology through big data multidimensional collecting enterprise information, establishing intelligent risk control process, establishing the core enterprise credit evaluation system, preventing the core and small and medium-sized enterprises collusion fraud, preventing the third-party logistics warehousing enterprise credit risk, and reducing the risk of commercial banks supply chain financial business.
Developments of Sensing Technology and its Applications in Virtual Reality
Authors: Wang Chang
Published: April 2023

Virtual Reality is one of the hot research directions recent years, as a 5G rea oriented technology and a new way of displays, virtual reality has shown us its huge potential in industries, especially in entertainment, medical science and education. Sensing technology is widely used on VR in many aspects such as the HMD displaying system, position and motion tracking module and the input devices like VR controllers, while most overview papers pay attention to the concepts and applications, though some of them illustrated all the technologies roughly. This overview article has focused on sensing technology in VR, introducing the techniques in some commercially successful VR devices, comparing and summarized some of the latest research based on them, with which it may help users and researchers understand VR devices better and choose the appropriate devices while using. In the end, the paper summarizes the whole paper and prospects the future development direction.
Music Similarity Estimation based on Feature Extraction and Deep Learning
Authors: Niu Yiyi
Published: April 2023

The field of music information retrieval (MIR) has developed rapidly in recent years, which provides a lot of convenience for people's entertainment life after combining with big data and deep learning. This paper studies music similarity estimation in the field of music information retrieval. To be specific, based on the deep learning of music feature data, the music similarity model (a classifier) is obtained to judge whether the music similarity reaches more than 50% (i.e., random model). According to the experiment, using the Siamese neural network to train the model with chroma features as input can achieve high accuracy. It will make it more convenient for music recommendation, humming recognition and plagiarism identification. In the future, more researches ought to focus on the real value instead of classifier to judge the similarity or at least the clustering model (multiple thresholds instead of two). Anyway, these results shed light on guiding further exploration focusing on similarity extraction of music in the context of the state-of-art deep learning scenarios.
Exploiting Regressive Model for Population Prediction in China
Authors: Li Jiayi
Published: April 2023

The demographics of China reveal a huge population, which amounted to around 1.4 billion people in 2022. However, evidence and authorities demonstrate that the Chinese population is about to shrink for the first time and the country’s total fertility rate keeps decreasing. It introduces great uncertainty to the future development of China. An accurate population prediction is important to estimate the economy, make reasonable policies, and stabilize society. In this paper, three machine learning models, including the polynomial regression, logistic growth model and Autoregressive Integrated Moving Average (ARIMA), are used to forecast the population in China. The effectiveness of different models on population forecasting is compared and analyzed. The results show that ARIMA performs the best, which is about a 0.34% error rate validated on previous population data. The prediction results demonstrate that the population in China will experience a brief rise and then enter negative growth. To embrace the population decline, the government should get prepared for the aging society and propose reasonable policies to increase fertility.
Design and Optimization of a New Index Finger Exoskeleton Robot with Underactuated Mechanism
Authors: Chen Zhonghan , Wang Yunhai , Wang Zihao , Zhang Kunwei
Published: April 2023

Spinal cord injury and stroke are the main diseases that lead to loss or decline of hand function, and hand rehabilitation exoskeletons can be used to help restore hand function. Due to the problems of traditional hand exoskeletons such as heavier weight, higher cost and limited mobility, this project aims to improve an index finger exoskeleton robot with an underactuated mechanism, improve its bending and extension angles, and optimize the robot to a certain extent. flexibility. At the same time, the genetic algorithm is combined in the control system to make all the control indexes reach excellent. This project establishes a 3D model through Solidworks, and at the same time proves the feasibility of improvement by establishing a kinematic model. In terms of design, the mechanism is adjusted according to the range of activities of MCP(metacarpophalangian joint) and PIP(proximal interphalangeal joint) to optimize the flexibility of the design model. In the aspect of control system, the genetic algorithm is combined with system identification and PID(Proportion Integration Differentiation)control, in order to reach the objective of establishing a more precise training model and finding appropriate PID parameters. This project has a positive effect on the medical efficiency of the underactuated hand exoskeleton.
FPGA Hardware Acceleration Design for Deep Learning
Authors: Shi Haochen
Published: April 2023

A type of artificial neural network called a convolutional neural network (CNN) can learn characteristics from a huge amount of data and performs very well in the field of large-scale image processing. CNN simulates the behavior of a biological optic nerve. In recent years, with the development of deep neural network algorithms and hardware technology, the current "CPU+GPU" model servers cannot meet the neural network structure in various fields, so a large amount of deep CNN accelerators based on the FPGA platform have gradually emerged. FPGA is beginning to be used in the fields of image recognition and natural language processing because of its programmability, high performance, high stability, high security, and low power consumption. Though FPGA has proven to have better performance, there is still room for optimization at the design level. Yolov3, as a classical algorithm, still consumes a lot of time and computational resources in actual operations. To address this problem, this experiment partially optimizes the Yolov3 algorithm by introducing the CBAM attention mechanism in the Yolov3 model and pruning the embedded system with different proportions using the Network slimming method. Finally, it is verified on a TX2 embedded device developed by Nvidia using the COCO dataset. The experiment finds that the precision, mAP, and the number of parameters of the optimized Yolov3 algorithm under different optimization strategies. It is shown that the Yolov3 algorithm still has more optimization strategies that can reduce the time required for computation and the memory occupied more effectively without any degradation in accuracy.
Sparse-Aware Deep Learning Accelerator
Authors: Li Yihang
Published: April 2023

In view of the difficulty of hardware implementation of convolutional neural network computing, most of the previous convolutional neural network accelerator designs focused on solving the bottleneck of computational performance and bandwidth, ignoring the importance of convolutional neural network scarcity for accelerator design. In recent years, there are a few convolutional neural network accelerators that can take advantage of the scarcity, but they are usually difficult to consider in terms of computational flexibility, parallel efficiency and resource overhead. In view of the problem that the application of convolutional neural network (CNN) on the embedded side is limited by real-time, and there is a large degree of sparsity in CNN convolution calculation. This paper summarizes the methods of sparsification from the algorithm level and based on FPGA level. The different methods of sparsification and the research and analysis of different application layers are introduced. The advantages and development trend of sparsification are analyzed and summarized.
Comparative Analysis of the Effect of Online Education and Game Education on Students’ Study
Authors: Cao Yuan , Liu Tao , Miao Yu
Published: April 2023

In the context of the Coronavirus disease 2019 (COVID-19) epidemic, offline learning is undoubtedly limited, so online education is becoming increasingly significant. The authors collected data related to online teaching and educational games and compared their advantages and disadvantages from the perspectives of learning engagement and popularity. It was found that online lectures and educational games scored high on the dimensions of students' learning motivation, understanding engagement, and enthusiasm to participate, especially online educational games, which perform well in stimulating students' interest in learning, enhancing their sense of belonging to the group, and creating a better learning atmosphere. However, users' popularity of online classes and online educational games varies significantly. In contrast to the vast number of users of online classes, educational games are not universally accepted as a way of education in China. It is analysed that the main reason is that Chinese parents and teachers are biased against learning through games. It is challenging to connect learning with games, especially in China. Therefore, it is recommended that the mixed teaching mode of traditional teaching as the primary and game education as the auxiliary can be piloted before large-scale promotion.
Research on the Application of VR in Games
Authors: Bian Shijie
Published: April 2023

From traditional 3D games to VR games, the technology in gaming has changed a lot. Vr brings people a new experience. It is used in various fields such as health care and education, but it is widely known for gaming. In order to explore the reasons why VR is widely used in the field of games, the principle of VR games and what they bring to people is the purpose of this article. This paper mainly discusses the application of virtual reality technology in games. It describes the facts of evolution of VR technology. Use this as fundamental element, several types of VR games were made out which provide different positive effects on players. There’s difference between the traditional game and VR game. VR game is not yet a mature technology but will have a bright future. People can infer that VR application in the game is very suitable and have lots of potentials.
Possible Solutions to Depression Using BCI Methods
Authors: Bai Lin
Published: April 2023

According to the research results of 2022, the blue book of depression in China, there exists about 95,00,000 depression patients in China and the number is still growing. Depression is becoming an increasingly severe disease. As it is generally believed that the etiology of depression is multifaceted, related to gender differences, chronic stress, dietary behavior, and the abuse of drugs and alcohol. Clinical treatment is mainly on drugs, but BCI methods cannot be ignored. Compared to traditional methods, the BCI methods like Electroconvulsive therapy (ECT), repetitive Transcranial Magnetic Stimulation (rTMS) or Deep Brain Stimulation (DBS), DBS has many advantages, for patients whose physical conditions are not very ideal, the side effect of drugs may lead to serious conditions. in this paper is mainly focused on the use of BCI including technical background, applicable crowd, taboos and sequels. In the end of the passage some data from clinical cases would be discussed to give a specific impression about the methods. Then a possible scheme would be apposed about a possible solution to avoid triggering the side-effect of ECT in clinical conditions.
Co-training by Experience Replay for Reinforcement Learning
Authors: Huang Yuyang
Published: April 2023

In this paper, to improve the efficiency of the reinforcement learning model to explore the environment and get better results, a new method which involves the co-training process in reinforcement learning by sharing the experience pool of each agent in the training process has been developed. In this method, agents can gain a better understanding of the environment since agents use different policies to make action and explore the environment. At the same time, this paper designed an agent called Hard Memory Collector by modifying the value function and combining this agent and a normal agent for co-training. As an experimental result on the ViZDoom platform, the model achieved better results than the original Duel DQN network in terms of score, steps used per game and loss value.
Performance Analysis of Model-Contrastive Algorithm
Authors: Liao Yuantao
Published: April 2023

Federated learning develops multiple clients with a universal framework platform to train deep learning models with excellent performance. During this process, the local data set is not leaked to the outside, making federated learning a powerful method to protect data security while solving the problem of isolated data islands. However, in some usage scenarios, different clients are holding local data sets which are non- Independent Identically Distribution, i.e., non-IID. There have been significant attempts made to overcome this difficulty, and one of these efforts is the model-contrastive federated learning framework, as known as MOON algorithm. The main concept is to make use of the similarities between the global and local models to adjust the local training. The final global model performs well with non-IID data. The paper aims to replicate and analyze this algorithm to provide data reference for subsequent use. The algorithm is tested on the MNIST data set to get its performance on the handwritten digit classification task. This paper processed the data set to make it non-IID, but the result achieved a high accuracy rate with an acceptable computational cost.
Regression Analysis of Song Popularity based on Ridge, K-Nearest Neighbors and Multiple-Layers Neural Networks
Authors: Dong Aoran , Qiu Ruizhe , Ye Zhen
Published: April 2023

Contemporarily, human beings are working on the implementation of artificial intelligence technology in the arts fields, where the music is one of the directions. Before humans can create a song with artificial intelligence, it is necessary to understand the song first. This research tries to find out the relationship between the song's popularity and several selected songs' physical parameters based on statistics and machine learning. According to the analysis, this research proves that there is no significant relationship between selected physical parameters and the song's popularity. In addition, machine learning algorithms also do not find the potential relationships between them. In this case, it is safe to conclude that creating the song by considering these selected physical parameters is meaningless. On this basis, scholars should try to find out what factors make the song popular in terms of analyzing songs differently. These results shed light on guiding further exploration of future music analysis and artificial intelligence in music fields.
U-Net-based Precipitation Predict by Cloud Map
Authors: Chen Tianyu
Published: April 2023

Many traditional precipitation prediction methods in meteorology nowadays require many types of data to be input as parameters. This research is to investigate ways to use deep learning techniques for precipitation prediction using only input cloud maps. This paper establishes a technical route for predicting rainfall through cloud map data using U-Net, and experiments. Rainfall models were successfully trained using U-Net and predicted.
How to Imagine the World with Text? From Text-to-image Generation View
Authors: Liu Jingyi
Published: April 2023

Words are an effective and convenient way to describe the world, but sometimes what the texts convey may be misunderstood by readers. The expression of pictures is more vivid, easy to understand and has no borders, but creating a painting often takes a long time. Text-to-image makes the two expressions complement each other: It makes every ordinary person a “painter”, so that they can feel the world, express themselves, and create more whimsy through many rich pictures. For this vision, technologists are trying their best to improve image generation models, which enables computers to generate high quality images with texts better. And they are solving some technical defects, for instance, sometimes the content of generated images is strange. In the future, text-to-image can be adapted to applications in AI such as computer-aided design, image editing, and be employed in the field of art such as movies and artworks, and then it may even make a big difference on people's life, enriching the public's spiritual world and conveying information by vivid images.
Machine Learning Algorithms for Speech Emotion Classification
Authors: Yao Chuqiao
Published: April 2023

Artificial intelligence products work by imitating the way humans think. ‘Communication’ is one of the most important links in the sustainable development of human society. Meanwhile, allowing artificial intelligence to communicate with humans is also one of the main aspects of scientific research. In recent years, the level of development of artificial intelligence has already made some smart products that can chat with humans when they are bored. It can be predicted that in the future, we can have robot pets to accompany us. In order to make companion-type products to be more similar to humans, it is necessary to implant algorithms that could recognize human’s expressions of emotion. this paper is introduced two methods that can help AI to recognize emotion in human speech. One is to recognize words that express emotion, and the other is to recognize the tone of speech to judge emotion. Finally, it discusses the feasibility of this algorithm in reality.
The Design and Implementation of Inclusive Astronomical Website for Visually Impaired and Autistic Kids
Authors: Jiang Jinqiao
Published: April 2023

In the era of the rapid development of the Internet and science and technology, more and more children get knowledge from the Internet. Many children hope to learn some astronomical knowledge from websites. However, some of them may have some degree of disability. This article will focus on children with autism or visual impairment, because hearing or physical impairments often do not have a big impact on reading the web. This article will describe how to design a website that is more suitable for children with disabilities by comparing the author's website step by step with the various parts of some existing representative astronomy websites. To get an inclusive website that makes it easier for children with visual impairment or autism to read and focus on. Lower the learning threshold of children with disabilities, so that those who are "different" children better acquire knowledge and integrate into normal life, and thus improve their interest in learning and self-confidence.
Finding Wordle Strategies That Can be Mastered by Humans
Authors: Zeng Hongyi
Published: April 2023

Wordle is a popular web game. Players use strategies and hints to figure out the correct answer of a daily puzzle. In general, there are several strategies to solve it efficiently, but which is the best depends on the game’s current states. This paper designed 4 individual strategies which is learnable for both computers and human players, and trained an AI based on Q-learning to solve the game automatically with a win rate of over 80%. The decision this AI made is only based on the word list, not including other information (e.g., frequency of appearance on the Internet). Based on the analysis, it can give players advices in various kinds of situations and offer useful combos. By using this AI players, it is feasible to figure out what is the best choice and improve their skills. However, it still cannot solve some rare situations efficiently, which may require more training or a new way of setting the game states.
Real Estate Price Prediction based on Supervised Machine Learning Scenarios
Authors: Liu Zhihang
Published: April 2023

House price prediction is one of the most common supervised learning tasks in the machine learning field, which makes it a perfect criterion for the effectiveness of different learning models. From basic regression models to neural networks, countless methods have been proposed to solve the house price prediction problem. In this paper, the focus is the performance of three regression models, linear, LASSO, and ridge. There will be a selected dataset of sold houses from the open-source website. The data will be explored and visualized for a better understanding and then implement the regression models for further testing. According to the analysis, the LASSO regression model can yield the most accurate prediction with 90.15% accuracy but need a specific λ value. The linear and ridge regression yields similar predictions with close to 90% accuracy. Therefore, the most effective model for the house price prediction problem is the LASSO regression model. Overall, these results shed light on guiding further exploration of the performance of different machine learning models.
Research of News Recommendation Algorithms based on Deep Learning
Authors: Li Zhiling , Sun Hongyun , Zhang Junxiong , Zhang Zeyu
Published: April 2023

Deep learning is the key development trend of news recommendation technology, which has been thoroughly studied by most researchers. In today's social, news recommendation has become a very essential way for people to acquire news. The fundamental idea and procedure of news modeling are covered in this paper's main body, and one of our research interests is how to leverage convolutional neural networks to create news recommendation technology. We also looked at the news recommendation evaluation index, which looks at things like satisfaction, accuracy, diversity, and innovation. Additionally, this paper analyses numerous traditional algorithms and contrast the benefits and drawbacks of each. We also outlined a number of the challenges that the current study has faced. To contribute to this research, we looked into the expected future evolution of news recommendation technology.
Blockchain: Research and Application
Authors: Chen Zixuan , Guo Haofeng , Zou Yuanben
Published: April 2023

Blockchain has been invented for years and it gradually become the most important technique in nowadays’ internet era. Blockchain technology has been widely used in various fields, such as finance, trading, logistics, currency, etc. This paper explains and demonstrates the main algorithm and application of the blockchain. Until now, the primary applications of blockchain are mainly focus on the digital currency such as ETH. The value of blockchain, however, is not limited by only the digital currency but all the aspect within the internet. For instance, based on these years’ social concern, the pandemic of COVID-19 is the most worried problem. The application of this paper concentrates on the connection between COVID-19 and blockchain. By relating the technique of blockchain, the security and data tracing of medical supplies have been insured. Moreover, the personal information security is maintained during the process of doing the covid-test and the trajectory of activity.
Multiple Traditional and Deep Learning-based Prediction Models for Quantity of Domestic Mail
Authors: Xing Ruochen
Published: April 2023

As the main role of domestic mail is gradually changing from the field of communication to the field of economy and culture, it is very important to provide relatively accurate volume prediction and feedback through the visualization process. This paper focuses on finding models with a high matching ability to achieve the purpose of prediction, and selects the best option by comparing multiple models. Eight different models ranging from simple to complex will be used in the following steps. More specifically, the predictive power of the models has steadily improved, from linear to curved, from constant to trend, from a single dimension to adding gradient factors to combining seasonal periods. The prediction principle is also gradually complicated. In the beginning, it focuses on the average, then weights the values in different stages to obtain the weighted average, and then makes a more refined prediction by means of single-layer convolution. To achieve the purpose of obtaining better results, it is of great necessity to introduce a self-learning prediction model, so Recurrent Neural Network (RNN) model and Long Short-term Memory (LSTM) model are chosen. In addition to obtaining the input data of this time, the RNN model will also combine the input data and output data of previous times to make the comprehensive prediction. The LSTM model further solves the situation that the RNN model cannot predict in some cases, by setting the limit to determine whether to keep the data better to fit the data with the trend. The data visualization results show that the performance of LSTM model is the most outstanding among all models, which can provide learners with more accurate prediction information.
AI Artist with Reinforcement Learning and Graph Neural Network
Authors: Luo Keliang , Wang Xinyi , Zou Qingfeng
Published: April 2023

Recently, researchers have had much progress in the field of AI painting, inventing advanced machine learning models like Stable Diffusion. However, past methods mostly neglect the clients’ customized requests and need for humanized service. Facing this weakness, this paper constructs an AI to receive text input and to literally paint the image in a humanized way with a customized style. It analyzes the clients’ style requests and breaks down the need into two parts, the painting style, and the painting technique. After generating the initial image with Stable Diffusion models, this AI pre-process it with graph neural networks to satisfy the need for customized painting styles. Then, Reinforcement Learning is implemented to train the AI to paint the image like human painters with distinct strokes. In this step, the strokes can be modified to mimic various painting techniques like oil painting, pencil, etc. By two aspects of the style, this AI artist provides clients with a personalized painting with and a video of the full painting process.
The Advance of Digital Signature with Quantum Computing
Authors: Xu Zhanji
Published: April 2023

A digital transaction's sender or receiver's identity is verified using digital signature technology, a type of encryption. It allows users to prove their identity and the integrity of their message without relying on a third-party verification. Digital signatures are becoming increasingly important in the blockchain technology world. They enable users to verify the authenticity of digital assets, such as cryptocurrencies, and ensure that transactions remain secure. They also enable users to securely store their digital assets and prevent the unauthorized transfer of funds. Smart contracts, which are self-executing agreements that take effect when specific criteria are satisfied, also employ digital signatures. In addition, digital signatures are used to create digital signatures for digital documents, such as contracts, which allows users to securely sign documents without the need for a third-party verifier. This article presents the three primary digital signature algorithms presently utilized in blockchain technology: RSA, DSA, and ECDSA. It also discusses how quantum computers, which are currently being researched by some of the world's leading research companies, will affect traditional digital signature technology and what plans companies and countries have for this technology.
The Relationship between New Energy Vehicles and Environment
Authors: Liu Zheyuan
Published: April 2023

New energy vehicles (NEVs) are the focus of growth in recent years, people are increasingly concerned about the innovation of electric powered vehicles, environmental protection is now the main focus of people, is the main development of modernization of the way, green into people's lives, the national strategy tends to vigorously develop NEVs, so in recent years, China's new energy vehicle market soared rapidly. Nowadays, there is an important question arises which relate to the relationship between the energy vehicles and the environment, as well as the factors that affect energy vehicle sales.  The article will first give the reader a clear understanding of the data set under study through different data sets, then lead the reader to analyze the data and summarize the conclusions of the data through the same techniques of data mining or analytic techniques for data that are different used between different data sets, and finally explore the internal relationship between new energy vehicle sales, environment, policy subsidies, and other factors.
The Advance of Ethereum Digital Signature
Authors: Li Mengyao
Published: April 2023

Ethereum digital signatures have been found to make progress, but there has yet to be a consensus regarding their significance and process. Using digital signatures in Ethereum ensures the security of users' money and the Safety of their transportation. Elliptic Curve Digital Signature Algorithm (ECDSA), private key, and public key play an important role here. This paper analyzes the details of Ethereum's Digital Signature based on existing information. First, it explained the general background information about Ethereum. Send explain the ERC-20. ERC-20 are digital assets that represent value. They could become more compatible with the new standard and allow other features. Then digital signatures are explained in detail. This part included ECDSA, private key, and public key. ECDSA is the most critical algorithm during the production process. Private and public keys are the key features to ensure Safety. The last part is the Keystore file system. Using the Keystore file system, users can secure their private keys for their cryptocurrency wallets. To access their account, the user must have a password generated by them. The unencrypted files, on the other hand, create a vulnerability in the key store, which is exposed by these unencrypted files. Once an attacker obtains the user's unencrypted key, he cannot only take control of the account but also sign transactions on behalf of the user.
DQN for Coordinating Multi-agent Cooking
Authors: Zhang Yiwei
Published: April 2023

Reinforcement learning (RL) is a very widely used field. The difference between RL and other branches of machine learning (such as supervised learning and unsupervised learning) is that RL centers on interactive learning. The RL model (also known as the agent) learns in interaction with the environment to maximize the reward function. In the paper "too many cooks," the authors developed a method called Bayesian Delegation to enable human-like coordination by inferring the sub-tasks of others quickly. However, limitations still exist in the partial order of sub-tasks. First, implementing the sub-task in terms of efficient actions or which agent(s) should work on it is not specified. Second, the sub-tasks may be finished in many different orders since the ordering of sub-tasks is partial. The project proposes solutions to these challenges using Deep Q-Learning (DQN) and Bayesian Inference. In the DQN experiment, value approximation performs well in the simple multi-agent environment.
E-Commerce Recommendation Algorithm based on Graph Neural Network
Authors: Zhu Lingxiao
Published: April 2023

While recommender system is becoming an increasingly essential component in e-commerce websites, although previous models, which directly calculate the similarity of user/item history record, has obtained evidence of effectiveness, recommendations based solely on users' current sequence of actions, when user identity and history preference are not present, has been a popular area due to the growing privacy concerns. This paper demonstrates a model using a graph neural network, which takes the user's sequence of purchasing events as input and constructs a graph derived from it, to make the prediction of the most likely subsequent product that the customer may purchase and make personalized recommendations by the combination of session preference and user’s current interest. Experiments on a real-world e-commerce purchasing event dataset and analysis are carried out to test the model’s performance, as well as how the length of sequence may affect the model preference. The result shows that the model performance has attained a local peak on the dataset used.
Studies Advanced in Image Style Transfer based on Deep Learning
Authors: Deng Haoyin , Lin Haoyu , Guo Yibing
Published: April 2023

A popular project in computer vision in recent years is called "image transfer", which attempts to create new images containing features from abstract properties of the content and style of a given image. Convolutional neural networks have generated the basic components of image style transfer, significantly improving transfer accuracy and visualization effect, thanks to deep learning's quick progress. The purpose of this study is to highlight the most recent developments in the area of deep learning-based picture style transfer. We first provide a detailed introduction to the traditional style transfer algorithms from the frameworks of generative adversarial networks and convolutional neural networks., including the design ideas research issues, key steps, advantages and disadvantages. On a few application tasks, we qualitatively compare the visualization outcomes of various approaches, and we talk about the subject's current issues and potential future developments.
Researches Advanced in Image Recognition based on Deep Learning
Authors: Yi Zheke
Published: April 2023

In the discipline of computer vision, which tries to create a model to predict the category of objects present in a given image, image recognition has always been a research hotspot. Early image recognition models were mainly based on manual features, and their recognition accuracy and generalization ability often fluctuated greatly with changes in the scene, which could not meet the actual application requirements. Convolutional neural networks have progressed quickly, which has accelerated the development of deep learning-based image recognition. in this paper, we detail the development of image recognition technology. Specifically, we introduce the three approaches of convolutional neural network, recurrent neural network, and graph neural network as the traditional methods of image recognition. including their design ideas, key steps, advantages and disadvantages. We also compare the recognition accuracy of different methods on the ImageNe dataset to try to explore the application boundaries of different methods. Finally, we go over the difficulties in the area of image identification and project its future growth.
Interest-based STEM Education for Children
Authors: Hu Yuxuan
Published: April 2023

The demand of STEM (science, technology, engineering, and mathematics) education popularization is growing. Nowadays, the STEM education should be focus, especially on children. Children should be known about STEM as early as possible, even to learn some basic knowledge. Using Participatory observation method to keep kids’ interest in STEM education, during education this study met some problem and demand. Through research, observe, and test, this study showed the interest will be the most important in STEM education. The various performances and behaviors displayed by the students helped the study to analyze, investigate and summarize. The purpose of this research will get some information and experience from children, to observe the interest and understanding of children with STEM. Using this result can make a plan for kids’ education on STEM in the future. In the meantime, this study can help educator to get more understanding for the children's mental processes in study.
Design Guideline for Medical Consultation Website for the Elderly
Authors: Ge Linsheng
Published: April 2023

With the development and improvement of computer networks, the online medical consultation is gradually becoming popular. More and more people choose to use online medical consultation to conduct early diagnosis or learn about disease related information and coping methods. The importance of online medical advice has become even greater as the elderly face many health threats and the current pandemic limits people’s normal travel. Many elderly people have pain points with diseases but no place for consultation and purchasing medicine. However, due to the difficulty of using electronic products for the elderly and the lack of design of existing products for the elderly, most older people rarely use online medical services. Most of the existing medical consulting websites lack the elderly centred design, which is inconvenient for the elderly to use in many places. Based on the data statistics and analysis of the questionnaire, user journey chart, pain points and comparative analysis of existing related products, this paper summarizes some reference and guideline during designing website for the elderly in the future.
A Quantitative Study on Carbon Sequestration - An Analysis of Changbai Mountain Forest Example
Authors: Lu Xiya , Wang Hansheng
Published: April 2023

Climate change presents a massive threat to life as we know it. To mitigate the effects of climate change, we need to take drastic action to reduce the amount of greenhouse gases in the atmosphere [1]. Most informed analysts now believe that the climate is warming. First, we engineer an ideal model, which the forest in the theory is a healthy, expanding forest free of fire and other natural calamities, and the plant biome remains intact. The carbon sequestration of the forest per unit area is considered to be A, implying that the total carbon sequestration of the forest is C1=A*S without any tree removal. we divide into two parts, one is exceeding cut of forests, and the other is cutting half of the forests. Then, we can calculate the total of the carbon storage. In addition, we design two specific models——the age of trees and types in Changbai Mountain forest. By using existing data, we can make several line charts, and we can know the concrete ranges, which could find the best forests' managemant. Through this way and our model, we can know what is the spectrum of management plans that your decision model may suggest? Are there any conditions which would result in a forest that should be left uncut. Are there transition points between management plans that apply to all forests? How are characteristics about a specific forest and its location used to determine transition points between management plans.
The Effects brought by Climate Change on Coral Reefs Ecosystem
Authors: Chen Wenxin , Liu Yiding , Yao Shunyu , Yuan Acacia
Published: April 2023

In the following conducted investigation, our group members focused on two issues which are closely related to Climate change and the impact on coral reef ecosystem. The first research question is: how climate change influences the coral reefs, and the second question investigated is how climate change impacts on the living habits of organisms lives among coral reefs. There have been great many research projects done within the relative subjects, therefore, our investigation method is to collect the data from previous investigations and develop new understandings based on them in response to our research questions. Through research and investigation, we concluded that the three main types of influence climate change can have on coral reefs are bleaching, oceans acidification and change in the weather patterns, while for the question of aqua organisms’ living habits, the change in species’ habitat is predicted based on existing research. When approaching the end of research study, why protecting coral reef ecosystem is significant was discussed, and proposals of improving the current situation are made.
Research on glass classification based on linear regression model
Authors: Wu Ru , Zhang Yilin , Zhao Xukang
Published: April 2023

This paper studies the rule of chemical composition of ancient glass products and the identification of glass types. Through Spearman correlation analysis, the establishment of multiple linear regression model and Fisher linear discriminant model, the relationship and classification rule of chemical composition of different types of glass are determined, and the linear regression model of chemical composition content before and after weathering and the identification method of glass types are obtained. The results show that the mathematical model proposed in this paper can effectively identify glass types and provide guidance for the identification of glass components.
Glass classification study based on principal component analysis and systematic clustering
Authors: Shao Junyang , Wang Zimin , Wang Kangqi
Published: April 2023

Ancient Chinese glass is similar in appearance to Western glass, but the chemical composition and its content are very different. In order to properly explore the classification laws of different categories of glass and further study their subclassification, this paper starts from the sample data and uses systematic clustering and principal component analysis to develop a glass classification study based on the index Euclidean distance.
Nanomaterial-based Clinical Testing and Diagnostic Technologies
Authors: Jiao Ruiqi
Published: April 2023

With the development of medical and health care, clinical medical practice has put forward higher and higher requirements on the performance indicators such as sensitivity, detection limit and analysis speed of in vitro diagnostic technologies. While traditional in vitro diagnostic techniques are increasingly unable to meet the demand, in vitro diagnostic techniques based on nanomaterials have attracted more and more attention and research in combination with their high sensitivity and high analytical speed. This review summarises the commonly used nanomaterials, such as AuNPs, MNPs and QD, and their applications in the in vitro detection and analysis of proteins or nucleic acids. The methods mentioned in the text are proximity ligation assay, BCA, nanochromatography, nanopore sequencing and so on. In order to provide an introduction to nanomaterial-based clinical testing and diagnostic techniques, and new ideas for improving the use of nanomaterials in disease diagnosis. Due to their high sensitivity and quick detection, nanomaterial-based in vitro diagnostic methods offer a lot of potential for clinical use. But it still faces certain barriers to entry into clinical applications. It is foreseeable that these technologies will to enter clinical applications in the near future continuously and will play to their strengths to reach various clinical demands and better serve humanity. This is due to the growth of nanoscience and medical test science, as well as the appearance of diagnostic companies and regional test centres in vitro.
Nanotechnology in Cancer Diagnostics and Therapeutics
Authors: Li Jinhan , Liu Jinxu
Published: April 2023

In each and every nation on the planet, cancer continues to be one of the main causes of mortality and a serious impediment to the advancement of efforts to extend the human lifespan. Now, the growth of nanotechnology has led to new ideas and approaches in the detection and treatment of cancer. These new ideas and methods were developed by researching and developing the one-of-a-kind features of materials at the nanoscale. In terms of detection and therapy, the effects that nanotechnology has had on cancer are discussed in this research, including the use of gold nanoparticles, electronic noses and gadolinium (III) oxide nanoparticles in diagnostic imaging as well as analysis of tumor-targeted therapies and nanoparticle drug transport, and concludes with a summary of the advantages and potential risks of nanoparticles. In general, nanotechnology has the potential to improve the sensitivity of detection methods, the accuracy of diagnostic results, and significantly boost treatment outcomes, thus opening up a new research avenue for the field of cancer science.
Effect of Soil Microorganisms on Carbon Fixation Capacity of Biochar
Authors: Jia Yixing , Qian Bisha , Wang Zhiyu
Published: April 2023

In recent years, the impact of climate change caused by greenhouse gases such as carbon dioxide, nitrous oxide and methane has led scientists to pay attention to the use of biochar. As a new type of fertilizer, biochar has been successfully applied in agricultural production and soil remediation. It can help remove greenhouse gases from the atmosphere, convert them into very stable forms, and store them in the soil for thousands of years, thus realizing carbon sequestration. The carbon sequestration capacity of soil can be influenced by soil microbes and biochar. Therefore, it is important to study the effect of soil microbes and biochar on biological carbon sequestration for mitigating greenhouse effect in the future. It is also necessary to clarify the relationship between soil microorganism and biochar for the effective utilization of biochar and the enhancement of soil fertility. In this paper, the mechanisms of carbon sequestration by microorganisms and biochar were summarized on the basis of previous studies, and the interaction mechanism between biochar and microorganisms was summarized. With the joint effort of microorganisms and biochar, carbon fixation capacity and efficiency can be significantly improved.