bluuebunny/crossref_metadata_2025 huggingface dataset:
PENGARUH PENGGUNAAN MEDIA AUDIO VISUAL DAN MOTIVASI BELAJAR SISWA TERHADAP HASIL BELAJAR MATA PELAJARAN AKIDAH AKHLAK DI KELAS VII SMP MUHAMMADIYAH WATUKELIR SUKOHARJO TAHUN AJARAN 2021/2022
Authors: Agustin Reyna Yulia , Nashir Muhammad Ja'far , Fatchurrohman Muhammad , Fatimah Meti
Published: April 2023

This study aims to determine how the influence of the use of audio visual, learning motivation, student learning outcomes and is there any effect of using audio-visual media and learning motivation on student learning outcomes. This type of research is quantitative research. The subjects in this study were 7th grade students of SMP Muhammadiyah Watukelir Sukoharjo. Data collection techniques used in this study were interviews, questionnaires, observation and documentation. While the data analysis using multiple linear regression. Measuring instrument validity test uses the Product Moment Person correlation, and the reliability test uses Cronbach's Alpha formula. The analysis requirements test consists of a data normality test, a linearity test and multicollinearity test. Hypothesis testing using multiple linear regression analysis. The results of this study show that audio-visual media and learning motivation have an effect on student learning outcomes.
The Cause Analysis and Teaching Countermeasures of English-Speaking Anxiety Among Chinese University Students in The Context of Educational Psychology
Authors: Feng Xiangnan
Published: April 2023

Chinese university students often approach the English language learning process from a test-taking perspective and lack speaking skills. English-speaking anxiety is an issue that cannot be ignored when it comes to developing students' English-speaking skills. This paper investigates the speaking anxiety of 51 Chinese university students through a questionnaire and analyses the current situation and causes of speaking among Chinese university students in relation to three theories of educational psychology: behaviorism theory, cognitivism theory, and constructivism theory, and offers teaching countermeasures and suggestions. This paper has come up with two points. The first point is that in the current state of speaking anxiety, the greater the degree of English proficiency, the lower the level of speaking anxiety among Chinese university students. The second point is that peer pressure, their own language skills, the pressure to speak alone in speaking courses, and the pressure on instructors' attitudes in class all contribute to Chinese university students' speaking fear.
Promoting Positive Psychology in Senior High School
Authors: Liu Baotong
Published: April 2023

Nowadays, many high school students face massive pressure from studying and have less time for them to sleep, which causes them to have mental issues that need to be solved, such as depression or anxiety. Previous research has established that positive psychology is associated with broad attention, creative thinking, and holistic thinking, whereas negative emotions restrict focus on narrow attention in high school students. Based on positive psychology, this paper discusses an effective method of solving this issue. This paper takes 100 senior high school students as research samples through a questionnaire survey and observation method analysis. It is found that the positive psychology curriculum has played a positive role in the education of senior high school students. It proves that positive psychology is applicable in high school classrooms, although some improvements need to be made. This paper could help high schools and other institutions that educate students under 15 to above 18 to figure out a method for them to reduce the likelihood of high school students having mental problems through positive education.
Online Gamification Learning During The COVID-19 Pandemic
Authors: Wei Xiaoling
Published: April 2023

The unexpected COVID-19 outbreak in 2019 pushed most schools to switch to online teaching styles, and students had to adapt to online learning at home. Using case analysis and literature review, this study is committed to sorting out and studying theoretical studies and case studies of gamified online learning in COVID-19 time. This study will trace the meaning of gamification and analyzes related literature and closely related theories such as Motivation Theory, Serious Games and Flow Theory, which have some internal correlation to gamification. English studies are taken as case studies to inspect the effects of online gamification learning during the worldwide COVID-19 pandemic. This study concludes that gamification learning, although has some shortcomings in class, can not only effectively promote students' classroom engagement and desire to learn behaviors, improve students' learning motivation and classroom interaction, but also shorten the social distance between teachers and students and alleviate people's depression and other bad mental emotions.
The Impact of Double Reduction Policy on the China’s Education System
Authors: Kong Yunxi
Published: April 2023

On July 24, 2021, the General Offices of the CPC Central Committee and The State Council issued the Opinions on Further Reducing the burden of homework and off-campus training for Students in compulsory Education (referred to as the "double reduction" policy). This policy has since dramatically changed the landscape of shadow education in China and has impacted millions of people, including those in the education industry. Chinese shadow education has been decimated and has taken a significant negative hit, requiring rapid transformation and upgrading to meet the challenge. While this policy has had a positive impact, which resulting in a significant reduction in stress for elementary and middle school students in the K-12 education system, however, the need for abundant educational resources for students has still not been effectively addressed. And this situation will continue to intensify as China's fertility rate continues to decline. This paper describes the description and impact analysis of the policy and uses the education reform in Japan as a research precedent to provide possible effect prediction and improvement options for the double reduction policy.
Exploration of China's Internet Finance Business Model: Taking Ant Financial Services Group as an Example
Authors: Zhang Jixin
Published: April 2023

In today's rapidly developing Internet economy, China, as one of the most developed countries in the Internet industry, has seen the emergence of many competitive Internet finance companies. Compared with the traditional banking system, these Internet finance companies have greatly facilitated people's lives and provided them with more comprehensive financial services. In order to explore the business model of China’s Internet finance companies and provide suggestions for their future development, this paper analyzes the Ant Financial Services Group, the largest Internet finance company in China. Through exploring the business model of Ant Financial Services Group and collecting relevant data, the advantages of its business model are analyzed, and suggestions are made for the future development of Ant Financial Services Group and China's Internet finance industry, this paper suggests that Ant Financial Services Group should expand its overseas market, lay out social field, focus on its Fin-Tech positioning, pay attention to relevant policies and reduce its dependence on Alibaba Group. For government departments, the legal system for Internet finance industry should be improved as soon as possible to regulate the development of Internet finance industry. By reading this article, readers can gain a deeper understanding of the current situation of Ant Financial Services Group and the development of Internet finance in China.
Impacts of Global Warming on Economy
Authors: Li Chengcheng
Published: April 2023

Global warming is the rise in temperature of the Earth's surface and its atmosphere, resulting from human activities. It is caused by the greenhouse effect, which can be described as an increase in heat trapping gases such as carbon dioxide and methane in Earth's atmosphere. Global warming has been named a major factor for raising global sea levels through its effects on oceanic thermal expansion, melting glaciers and polar ice caps, changes in ocean patterns due to global warming. All of these changes will have an impact on economy and this paper will investigate each factor’s impact. Global warming has had and will have many impacts on the economy. If global warming is to be stopped future generations should not have to pay for the pollution and other effects that occur as a result of global warming caused by human activity. Economies will be changing in response to far less energy being produced by fossil fuels, and therefore a decrease in economic output. People will also think about more renewable energy production systems in their homes, such as a solar panel. This paper will focus on the negative impacts that arrive because of global warming caused by human activity.
A Regularized K-means Autoregressive Shrinkage Method to Portfolio Optimization
Authors: Li Xinyu
Published: April 2023

Estimation is always an issue of portfolio optimization. To address this issue this paper started from addressing global minimum variance (GMV) portfolio, which gives the weights as the function of estimated covariance matrix of the asset net returns. The inverse operation of the matrix always leads to ill-conditioned matrix due to the noise in the return series. Therefore, regularized sample covariance matrix is often used to address this problem. In this work, a regularized autoregressive shrinkage method for covariance matrix estimation with the historical data is developed, which regards the future covariance matrix as the function of the historical covariance matrix. K-means clustering algorithm is also added to enhance the performance of the regularized autoregressive model. The results demonstrate that the performance of GMV portfolio can be enhanced with regularization under autoregressive shrinkage model.
Walmart Sales Prediction Based on Decision Tree, Random Forest, and K Neighbors Regressor
Authors: Yao Bo
Published: April 2023

Sales forecasting is a very important research direction in the business and academic fields, and sales forecasting methods are also in full bloom, such as time series model, machine learning model and deep neural network model. This paper will use three machine learning models: Decision Tree Regressor, Random Forest Regressor, and K Neighbors Regressor to predict Walmart Recruiting - Store Sales data. Using correlation, mean absolute error, and mean square error to evaluate the prediction results of these three models, it is found that the prediction effect of Random Forest Registrar performs the best of these three models. The R2 value between the predicted sales volume of Random Forest Regressor and the sales volume of the test set is 0.937, the average absolute error is 1937.810, and the mean square error is 32993323.634. Therefore, Walmart can use Random Forest Regressor when forecasting the weekly sales of its own stores. At the same time, this paper provides a good model reference value (especially Random Forest Regressor) for other industries when researching the sales forecast, as well as methods for evaluating different model predictions. Overall, these results shed light on guiding further exploration of Sales forecasts for supermarkets.
A Coexisting Strategy of Economic Development and Environmental Protection
Authors: Zhang Jianbo
Published: April 2023

It is impossible to separate economic development from industrial development. It should be noted, however, that industrial development has a detrimental effect on the environment. Consequently, pollution of the environment will require a large number of workers and material resources to control, which is contrary to the original intention of economic development. The development of economic activity should not ignore environmental protection but should strive for a common development of economic development and environmental protection and integrate environmental protection awareness into economic planning so that economic growth and environmental protection can be mutually beneficial. This type of economic development is considered low-level, assuming that environmental resources are consumed without limit, and environmental governance issues are ignored. Our organization pursues a high-level and sustainable economic development strategy. Therefore, we need a high-level and sustainable strategy for economic development. As a result, we hope this paper will serve as a reference for those interested in symbiotic development that considers economic development and environmental protection as research subjects.
Analysis of the Per Capita Carbon Emission Effect of China's OFDI to Countries Along the "Belt and Road"
Authors: Peng Yiru
Published: April 2023

Under the "Belt and Road" initiative, China adheres to the principle of co-business, construction and sharing, and the exchange and cooperation with countries along the route as well as the economic and trade relations are getting closer and closer, and the OFDI between China and countries along the " Belt and Road" is growing steadily. This paper uses a combination of theoretical research and empirical analysis to study the per capita carbon emission In the theoretical analysis, a theoretical model of carbon emissions and China's OFDI is constructed. In the empirical analysis, firstly, the stock of Chinese OFDI is taken as the core explanatory variable. Then, based on the availability of data, the cross-country data of China's OFDI to 76 countries along the "Belt and Road" from 2009 to 2019 are selected. The relationship between China 's OFDI and carbon emissions was also introduced as control variables. The relationship between China 's OFDI and carbon emissions is analysed empirically through an extended individual time double fixed effects model. The findings of this paper are as follows: (1) the regression coefficient of Chinese direct investment (1) the regression coefficient of Chinese direct investment in the coastal countries is significantly negative, indicating that Chinese OFDI suppresses carbon emissions per capita in the coastal countries; (2) GDP per capita significantly contributes to (2) GDP per capita significantly contributes to the increase of carbon emissions in the coastal countries; (3) the impact of labour force level on carbon (3) the impact of labour force level on carbon dioxide emissions per capita in the coastal countries is significantly negative, i.e. the increase of labour force level can effectively reduce carbon emissions in the coastal countries; (4) The impact of industrial structure on per capita CO2 emissions in the countries along the route is significantly positive. Finally, the policy implications are as follows.
Study on the Influence of Green Credit Policies on the Investment Efficiency of Heavy Polluters
Authors: Zhang Zhihan
Published: April 2023

Green finance is an important driving force for green innovation as well as green development in China, and is also one of the important strategies for the current high-quality economic development. In the dual context of building a green financial system and a market-oriented green technological innovation system, this paper takes the implementation of the Green Credit Guidelines in 2012 as an entry point, and uses a two-way fixed double difference model to test and examine the specific effects of the implementation of the Green Credit Guidelines policy on the investment efficiency of heavily polluting enterprises based on panel data of Chinese listed companies in Shanghai and Shenzhen A-shares from 2009 to 2020, and analyze the mediating mechanism of financing cost and debt maturity. It is found that (1) the implementation of the Green Credit Guidelines significantly improves the corporate investment efficiency of heavy polluters; (2) the loan amount and debt maturity play a partially mediating role between the Green Credit Guidelines and the investment efficiency of heavy polluters; and (3) the implementation of the Green Credit Guidelines improves the investment efficiency of heavy polluters in state-owned and non-eastern regions more significantly. The results of the study further emphasize the necessity of implementing green financial policies and the importance of implementing the policies according to local conditions so as to contribute to the high-quality green development of China's economy.
Exploring the Future of Electric Vehicles in China: Market Trends, Government Policies, Carbon Emissions and Technology Development
Authors: Liang Yuling , Li Yuanzhe
Published: April 2023

China is a leader in the production and adoption of electric vehicles (EVs), with a rapidly growing market and supportive government policies. With a goal to reduce air pollution and dependence on oil, China has set a target of having 5 million EVs on the road by 2020 and is taking various steps to encourage the development and use of electric vehicles. The Chinese government has established a number of policies to support the development of EVs, including tax incentives, subsidies, and the development of charging infrastructure. In this paper, market trends, government policies, carbon emissions and technology development have been systematic analysed, China's strong commitment to the development of EVs is expected to play a key role in shaping the future of the automotive industry. Design students can play a role in reducing carbon emission by designing lightweight, aerodynamic, and sustainable materials cars. EVs have the potential to significantly reduce carbon emissions from the transportation sector and will continue to gain market share in the coming years, but it is unlikely that ICEVs will be completely replaced in the near future.
Understanding Tesla's Financial Strength: Analysis of Financial Reports and DuPont Analysis
Authors: Huang Wei
Published: April 2023

Incorporated in 2003, Tesla motors, inc is a leading American automotive company named after electrical engineer and physicist Nikola Tesla. Tesla designs develop, manufactures, and sells high-end fully electric vehicles and advanced electric vehicle powertrains. Meanwhile, it has established its global selling network, service centers, and supercharger station. With a focus on sustainability and innovation, Tesla has set the standard for the industry and is leading the charge toward a brighter future. This essay will suggest the company’s history, products, and business strategy. Additionally, according to some previous research about the company, a financial analysis will be conducted to examine the company’s financial performance. The revenues, sales scale, income, capital structure, and key ratios will be shown in it. This analysis will also provide insight into Tesla’s financial stability and its earing ability in the future. This analysis will look at the firm's distinctive approach to technology, design, and marketing, as well as its impact on other organizations and sectors. Overall, the research will show that Tesla is not a vehicle firm, but rather a major pioneer in the fields of sustainable energy and transportation.
Check and Balance Overspending in Average American Household Consumption
Authors: Zhang Zihan
Published: April 2023

Overspending is a problem where people tend to over-excessive purchase unnecessary subjects, such as clothing, cars, and technology that does not match any mean of their income. This caused many people to diet, have psychological problems, and have unstable family relationships. However, the problem of overspending comes down to people don't recognize their spending habits. This research presents how credit cards have a major impact on people’s habits of overspending and social norms that caused overspending. Lastly, the research shows that financial literacy implementation is a crucial method for preventing overspending and that developing the habit of managing money at a young age is crucial to minimize the effect of overspending. Money management awareness is important in the education system.
Consumer Behavior Analysis from Bigdata Insights: Evidence from YouTube, Douyin and Bilibili
Authors: Li Haoran
Published: April 2023

Some businesses, in the age of intense rivalry and rapid expansion of big data, have started paying attention to the actions of their customers, using big data analysis in order to evaluate and keep tabs on their customers. By evaluating user activity using big data, businesses are better able to comprehend the requirements of customers and provide them with more individualized offerings. This article estimates the instances of YouTube, Douyin, and Bilibili in order to highlight the impact of big data on consumer behavior as well as the use of big data in the study of consumer behavior. In the area of OVP, three separate businesses used a variety of approaches to big data processing in order to carry out in-depth consumer research, and the outcomes of this study were all deemed acceptable. In conclusion, this article discusses some of the current limitations of big data as well as the prospects for the future, with the goal of helping as well as various insights to businesses if they decide to conduct in-depth research on customers based on big data technology in the future.
The foreign policy transition of the German Scholz government in the context of the Russian-Ukrainian conflict and its causes
Authors: Tian Huiyi
Published: April 2023

Following the outbreak of the Russian-Ukrainian conflict, German Chancellor Scholz delivered the Zeitenwende speech on February 27, 2022, announcing a series of measures including the supply of weapons to Ukraine and the strengthening of its own defense forces. This was a historic transition for Germany's foreign and security policy, which has always been pacifist. This paper will take the transformation of the German Scholz government's foreign policy in the context of the Russian-Ukrainian conflict as the object of study. And it is the purpose of this paper to analyze the reasons why this historical transition in foreign policy occurred in Germany at this time. In general, this paper specifically analyzes the Scholz government's foreign policy transition based on the analytical model of foreign policy transition proposed by Jakob Gustavsson, and argues that international factors, domestic factors, policy makers' factors, and the stimulus of the crisis are the four main factors that led to this foreign policy transition.
Mars Business Analysis
Authors: Xue Jianing
Published: April 2023

Mars Incorporated is a well-known confectionery company in the United States. It is famous for its chocolate snacks: Twix, Snickers, M&M’s, and so on. It also offers business all over the world. As an outstanding company, it’s valuable to analyze Mars’s business strategy and take a closer look at its performance in this industry. In the following analysis the paper will be discussing Mars’ performance relative to their business. The paper firstly describes the basic introduction of Mars company’s background as well as its mission, business strategy, and industry. Then, I will be analyzing Mars’s strengths, weaknesses, opportunities and threats compared to their industry competitors. Using information gathered on their top competitors to reveal how well Mars is performing comparatively. Also the paper will be providing recommendations on how Mars can be more successful.
Automatic Music Genre Classification based on CNN and LSTM
Authors: Luo Xining
Published: April 2023

Various applications of machine learning are discovered and receiving more and more attention contemporarily. The music industry has benefited from the incorporation of artificial intelligence, especially the field of music classification, as machines are able to organize big data in a more efficient manner than the traditional human expertises. This paper compares two machine learning models, the Convolutional Neural Network model (CNN), and the Long Short Term Memory model (LSTM), from their architectures, functionality, to classification accuracy based on empirical data. The models were trained on two datasets, GTZAN and FMA. The result indicates that the CNN model achieved a 56.0% and 50.5% accuracy for the two datasets respectively, outperforming the LSTM model, which had a 42.0% and 33.5% accuracy. The paper aims to analyze the two models’ capability for music classification and determine which model is better suited for the task. These results shed light on guiding further exploration of computer music.
Regarding the Immersion of VR, What do We Need to do to Make it Better?
Authors: Yuan Likai
Published: April 2023

Immersion is an objective factor for the virtual reality experience. It is important to VR experience. By analyzing the application of VR entertainment such as games and movies, the meaning of immersion is shown. Then looked at how to improve immersion through VR hardware, virtual design, and more. After a brief introduction to the definition of virtual reality, this paper analyzes the impact and importance of immersion through the application of two critical areas of VR entertainment, movies and games. Then, the paper roughly classifies VR hardware into two kinds of output and input devices and studies how to improve VR's sensory stimulation and sense of substitution through hardware. Finally, in the areas of sense of embodiment, reality experience, empathy, and socialization in VR environment design, the role of immersion in all aspects of VR entertainment and ways to enhance it are described. The article concludes with a summary and outlook on the development of VR.
Comparison of Machine Learning Strategies in Hazardous Asteroids Prediction
Authors: Wang Yao
Published: April 2023

The purpose of this study is to use multiple classification algorithm from machine learning to predict hazardous asteroids that orbit Earth. Seven models are Logistic Regression, K-Nearest Neighbor Classifier, Random Forest Classifier, Decision Tree Classifier, Multinomial Naïve Bayes Classifier, Gradient Boosting Classifier, and Voting Classifier. Confusion matrix is used to evaluate those models. Evaluation metrics include accuracy, precision, recall, and f1-score. The result shows that random forest classifier has the greatest overall performance with highest accuracy. Decision Tree classifier, Gradient Boosting classifier, and Voting classifier also perform well. Gradient Boosting classifier is capable of greatly reducing the risk of hazardous asteroid, which is, reduce the number of hazardous asteroids that is predicted as non-hazardous. Because of assumptions of some models like Logistic Regression, data used in the experiment do not follow them, so the overall performance of those models are not well. It would be better to select data for fitting the model. The result shows that combined classifiers perform better. Voting Classifier can be used to assemble those accurate models and get a more accurate result by offsetting disadvantages of each model.
Chatbot Improves the Customer Service in Four Important Industries
Authors: Zhou Xin
Published: April 2023

Chatbot was first introduced in 1966. With the development of Artificial Intelligence for decades, the function and usage of Chatbot have been enhanced to a very high level. In this paper, we are going to talk about how the use of Chatbot improves the customer service in four industries: healthcare, advisory, commercial, and education. They are the four important social fields, and they all have very large demands and uncountable customers every day. The human agents are facing tons of customers from various backgrounds. They need the help from artificial intelligence. Apart from that, we will also discuss some special advantages of AI-based chatbots which them work so effectively in customer service. These three advantages are automation, trust, and productivity. With the combination of these advantages, chatbot can be highly supportive in the field of customer service and improve the quality of customer service. Eventually, we will talk about some challenges the chatbots are facing now, and how to make it function better in customer service to meet more users’ expectations.
The Benefit of a Regression in VR Game
Authors: Luo Wenqi
Published: April 2023

Nowadays, the Virtual reality advances in a startling speed. More and more game company start to make VR games. There are five types of VR game: Non-immersive Virtual Reality, Fully Immersive Virtual Reality, Semi-Immersive Virtual Reality, Augmented Reality, Collaborative VR. However, this article only talks about semi-immersive virtual reality. Semi-immersive virtual reality which provides a three-dimensional space by headsets is the combination of non-immersive and fully-immersive virtual reality. This article will explore the relationship between Degree of Freedom and semi-Virtually Reality Game. Even though most of the people believe higher the Degree of Freedom is greater the game itself is, this work believes even in Virtual Reality Games, high or low Degree of Freedom server certain purposes but never defines a game’s quality. This paper made two versions of a VR game called In the Castle: one version is low Degree of Freedom, the other is high Degree of Freedom, and organized several experiments to explore the benefits of the low degree of freedom. The result of the experiments shows that the low degree of freedom in VR game can help players concentrate on the game and simplify the preparation.
Perspective and the Use of Eye Tracking in Human-Computer Interaction
Authors: Ding Guoting
Published: April 2023

With the increasing demand for information technology, the traditional human-computer interaction (HCI) mode using keyboard and mouse cannot meet the need, which has inspired the research of multi-channel interface based on new interaction technologies such as eye tracking. The direction of human eye contains the information of current task status, so eye gaze is one of the convenient input channels for HCI. To make eye tracking technology more comprehensive and widely used, it is constantly improved. With the development of eye tracking technology, it has been scientifically explored in HCI to record eye movements. The main purpose of this paper is to summarize the update of eye tracking technology currently used for HCI, summarize the development direction and shortcomings of the current technology, and help new researchers to study various methods and algorithms of eye tracking. This paper will briefly introduce the eye tracking technology, summarizes the current application of eye tracking technology in HCI, and discusses the future direction.
Developing a Single-Player Sci-Fi First-Person Shooter in Unity3D
Authors: Lyu Keang
Published: April 2023

First-Person Shooter is currently one of most popular game genres. However, most of the shooters are realistic and multiplayer oriented. Besides, there’s no single-player oriented FPS framework made for Unity3D which remains a small blank in Sci-Fi single player FPS game area in recent years. So, this paper developed a game named What Happened to Site-13? using Unity3D which is a realistic Sci-Fi FPS which is single-player oriented. The game contains a gamepad-oriented FPS Controller, Story-telling module, AI Module, an in-game map editor and scene with realistic sci-fi art style. Through tests with testers, to test if the game is immersive, and the combat loop is comfortable, the visuals meet expectations. After tests and validation, it was proved to achieve target art style, visual experience and combat loop, it also was proven to be immersive. Also, it can serve as a framework for FPS games after validation with a group of students.
Playing Flappy Bird with Two Different Value Learning Algorithms
Authors: Li Beining , Lu Yimeng , Mo Yunhao , Yu Weiqi
Published: April 2023

In this paper, reinforcement learning will be applied to the game flappy bird with two methods DQN and Q-learning. Then, we compare the performance through the visualization of data. Furthermore, more results from other games are summarized to analysis the corresponding advantages and disadvantages. Finally, we discuss and compare these two reinforcements learning methods.
Gold and Bitcoin Price Prediction based on KNN, XGBoost and LightGBM Model
Authors: Yuan Ziyang
Published: April 2023

In the past few decades, there has been an increasing demand for assets trading with help of machine learning. Contemporarily, the cryptocurrency and gold market has become prosperous with extremely dramatical fluctuations. This paper aims to study the trading price laws based on machine learning scenarios of Bitcoin and Gold to predict the price of the two currencies. To be specific, this study gives an inside view of the application of a method combined three algorithms (i.e., KNN, XGBoost and LightGBM) to predict the future Gold and Bitcoin price browser based on past data from 2017 to 2022. According to the analysis, the study shows the difference of three models, the accuracy of the combined algorithms and proves the related metrics to predict the price of the Gold and Bitcoin. Overall, these results give a guideline for the investor to make sensible decisions about Bitcoin and Gold price and shed light on guiding further exploration of price forecasting in terms of machine learning approaches.
Research of Multi-agent Deep Reinforcement Learning based on Value Factorization
Authors: Liu Shiyi
Published: April 2023

One of the numerous multi-agents’ deep reinforcements learning methods and a hotspot for research in the field is multi-agent deep reinforcement learning based on value factorization. In order to effectively address the issues of environmental instability and the exponential expansion of action space in multi-agent systems, it uses some constraints to break down the joint action value function of the multi-agent system into a specific combination of individual action value functions. Firstly, in this paper, the reason for the factorization of value function is explained. The fundamentals of multi-agent deep reinforcement learning are then introduced. The multi-agent deep reinforcement learning algorithms based on value factorization may then be separated into simple factorization and attention-mechanism based algorithms depending on whether other mechanisms are incorporated and which various mechanisms are introduced. Then several typical algorithms are introduced and their advantages and disadvantages are compared and analyzed. Finally, the content of reinforcement learning elaborated in this paper is summarized.
Research of Convolutional Neural Network on Image Classification
Authors: Lin Xiyu
Published: April 2023

With the progress of artificial intelligence, technology based on deep learning is becoming more and more mature, and the application of deep convolutional neural network for image classification has become a popular topic for researchers. The number of the structure of deep convolutional neural network for image classification is keep increasing, and its performance is consistently improving, gradually replace that of traditional methods. According to the process of model development and model optimization, this paper divides the convolutional neural network into two models: classical deep convolutional neural network model and attention mechanism deep convolutional neural network model. The construction methods and characteristics of various kinds of deep convolutional neural network models are comprehensively reviewed, and the performance of various classification models is compared and analyzed. Finally, the problems of deep convolutional neural network model are presented.
Analysis of Various Models for House Price Prediction based on Machine Learning
Authors: Wu Yuhan
Published: April 2023

The real estate prices prediction is a relatively popular data analysis problem. With the gradual increase in the competitiveness of the housing trading platform in recent years, and the basic model of house prices predicted is different from the factual transaction price. Therefore, it is necessary to design more accurate house prices prediction models. This article will adopt different linear regression models and machine learning models. By training these models of training Set, through the accuracy algorithm of Kaggle, the accuracy of each model is obtained. According to the analysis, this article finally discovered that the accuracy of the new model is improved than the accuracy of the basic model (i.e., new model predicts house price more precision), but it can be improved in the case of less data in the real environment. The reason may be related to economic and public opinion effects. Overall, these results shed light on guiding further exploration of house price.
Proof of Work vs. Proof of Stake in Cryptocurrency
Authors: Lin Shijie
Published: April 2023

This research document demonstrates the understanding of two key elements: Proof of Work (PoW) and Proof of Stake (PoS) within cryptocurrency. Cryptocurrency is often misunderstood as just volatile and risky but many investors do not understand what it even is. What is proof of work (BTC)? What is proof of stake (ETH)? How are they similar and how are they different? Cryptocurrency is actually more relatable that originally imagined once a person understands digital currency, money, it’s origins, and how they hold similar monetary value within their own wallets. Cryptocurrency in PoW involves complicated mathematical equation solving via mining from a large growing pool of miners. These miners receive rewards such as bitcoin/tokens/etc. from their mined hash blocks verified, and blocks added to the blockchain. Challenges are that there are many miners and there may be a computational limit per core of each PC to be able to mine since Bitcoin has increased its difficulty 70 billion times since it started. BTC or Bitcoin is still widely trusted due to its increasing difficulty to race to the finish line to finish the advanced math computations. Whereas within PoS this is not a race between the masses and advanced math computations, but a validator that generally has more put in their stake vs. gained by receiving the processing fees associated. ETH or Ethereum has shifted from PoW to PoS for its better energy efficiency in resources. It has employed many other additional checks from Casper to Gasper which is the combination of Casper (fork-choice algorithm), LMD-GHOST (heavest observed subtree), and finality which requires 2/3rd agreement as well as once a block is justified it is upgraded to a finalized block. the similarities between the two PoW and PoS are that they are consensus-driven algorithms. They are designed to reach an agreement between the systems in place before each block is placed in the blockchain. Additionally, they also have a shared public ledger that operates on a global if not international scale. With blockchains, there is always only one true version and this is relying on a network rather than a governing authority like a bank/government entity to provide security and to prevent fraudulent transactions. It is still widely contested which is better than these two well-known consensus algorithms and it is still widely contested. The best answer is suited on a per investor per business basis in terms of the willingness of taking a risk just like any investment. This document serves to help provide guidance in the understanding of cryptocurrency of two main consensus algorithms, its similarities, differences, and help identify see what a potential investor the reader may be.
The Study and Application of Facial Recognition Models
Authors: Feng Dihua , Peng Sixi , Wang Jiayong
Published: April 2023

Facial recognition technology is a biometric application that uniquely identifies or authenticates a person by comparing and analyzing facial features based on the width of a person's face. Face recognition technology has gained popularity recently thanks to the development and invention of artificial intelligence and other technologies. The traditional methods of face recognition technology development—the method based on geometric features and the method based on 3D models—as well as the deep learning Convolutional Neural Network (CNN) model VGG-Face for face recognition—are analyzed, compared, and described in this paper. It also provides a detailed description of face recognition technology. In this work, accuracy and TAR values are employed for geometric feature matching by comparing the metric results of each method on a dataset for better comparison. Our results show that while both traditional geometric feature modeling methods and 3D modeling methods can achieve good accuracy, however, they do not perform as well as humans in terms of accuracy. The Vgg-Face based on the deep learning model completely exceeds the 3D model method in terms of accuracy and TAR value, and is even more accurate than humans.
A Series of Models based on Long Short Time Memory for Temperature Prediction
Authors: Chen Tingxi
Published: April 2023

Given the increasingly serious climate problems that have been appearing around the world in recent years, as part of the climate research, finding ways of smart, accurate machine learning to carry out the temperature prediction is of great significance both for human activities and the earth's ecosystem. In this study, 3 kinds of the Long Short Time Memory (LSTM) models, which differs on the way how the data is input and output and the network structure, respectively Directly-Multiple-Output (DMO), Single-Step-Scrolling (SSS), and Convolutional Neural Networks Plus Long Short Time Memory (CNN+LSTM) are built and trained by Pytorch on the Jena Climate dataset to compare their predicted performance. The training loss of these 3 models are 0.0053, 0.1568 and 0. 0079; testing loss are 0.0048, 0.1764 and 0.0096; Mean Absolute Percentage Error (MAPE) are 3.42%,6.58% and 3.30%. The result turns out that CNN+LSTM is the best model in comprehensive consideration with the least convergence time, low loss and MAPE, followed by the DMO model with the least loss and low MAPE but longer convergence time. SSS performs worst with increasing high loss. In general, CNN+LSTM is suitable for temperature prediction while DMO is also good when short convergence time is not needed. SSS is not recommended for temperature prediction.
Prediction for the Extent of Cancer based on Multiple Machine Learning Algorithms
Authors: Chen Shujun , Wang Yuchen , Yang Ziang , Zhang Zezhou
Published: April 2023

Machine learning is a very important method for predicting the extent of cancer. In recent years, there has been considerable progress in detecting tiny features of the body, such as the specific surface area of cancer cells, the nuclear volume and perimeter of cancer cells. There is not development in measuring macro symptoms, such as Coughing of Blood, Chest Pain. Because in some remote and backward areas, detailed data are difficult to obtain, macro features need to be considered. In this paper, we use some relevant characteristics and environmental factors to predict the degree of cancer. During data preprocessing, some irrelevant contents are deleted, and the training set and the test set are divided. At the same time, some text data are digitized. Then, Naive Bayes, Decision Tree, Random Forest and Support Vector Machine were used in turn to make predictions and record their respective results. As a result, Naive Bayes, Decision Tree, Random Forest and Support Vector Machine achieved a fairly high prediction rate. More relevant features are obtained through the Person correlation coefficient and Feature Importance. On this basis, the prediction will not only maintain a high prediction rate, but also greatly reduce the memory consumption and training time.
Facial Expression Recognition based on Mini_Xception
Authors: Lyu Sizhen
Published: April 2023

As the premise of emotion recognition, facial expression recognition has received extensive attention and discussion, which aims to capture face information by computer, understand and classify it according to the way of thinking of people to improve the effect of human interaction. Through previous efforts have significantly improved the accuracy, few of them consider the application effect in specific scenarios. In this paper, we make a summarize of the existing research methods, and explore the generalization ability of the network model in different scenarios based on the MiniXception network. We also explore the scope with smaller or bigger impact on network performance, and make assumptions and prospects for the optimization method and development direction in this scenario.
Human Pose Estimation based on HRNet and Feature Pyramids
Authors: Dong Fangjie , Yang Miaowei , Yang Pengshen
Published: April 2023

A popular area of research in the realm of computer vision is human pose estimation, which is the process of recovering human joint points from the given images or videos. Convolutional neural networks, which have great feature representation capabilities, have become a fundamental component of human pose estimation algorithms as a result of the deep learning field's quick development. To enhance feature quality and raise the precision of human pose estimate, in this paper, we propose a new model based on HRNet neural network and feature pyramid to more accurately capture each main part of the human body. The model uses HRNet as the backbone network, taking advantage of its ability to combine low resolution with high resolution, and afterwards adds characteristic pyramids to the HRNetnetwork in order to enhance the capability of detecting small objects by utilizing its ability to solve multi-scale problems. The model can estimate a human's pose accurately, according to experimental results, and on the MPII Human Pose dataset, the addition of the feature pyramid enhances the model's detection performance by 0.05. in order to achieve smoothing after adding feature mapping with different resolutions, the detection performance improves by 0.17 after further improvement by adding  convolution. For the elbow, it improves by almost 0.5. The model's ability to increase the precision of human pose estimate is supported by all of the outcomes.
Application of Different Artificial Intelligence Methods on Reversi
Authors: Han Yiwei
Published: April 2023

Using different algorithms in artificial intelligence to perform adversarial agent games. The most popular adversarial game that could be implemented using artificial intelligence algorithms is chess games. The algorithm this research used includes minimax with improvement and deep reinforcement learning. The goal for this research is to compute the popular game Reversi in different artificial intelligence methods successfully. Moreover, the research seeks for improvements in the heuristic part of minimax algorithm and the combination of deep reinforcement learning with Monte Carlo Tree with Neural Network. This paper uses Reversi as an example to analyze different algorithms, including Minimax, Monte Carlo Tree, and Neural Networks. As a result, both algorithms work successfully. The alpha-beta pruning minimax algorithm with improvement in heuristic function and fixed depth cut-off significantly increase the winning probability and time cost of our artificial intelligence agent. The deep reinforcement learning successfully combined MCTS with neural network to train two agents to complete Reversi with great winning probability.
The Application of Artificial Intelligence Technology in Physical Training
Authors: Li Zhixiang
Published: April 2023

Athletics have become a key development area. Through a large number of trainings, our universities train sports talents for society, and encourage sports talents to go to the world stage to Strive for the glory of our country. In physical training activities, the use of artificial intelligence technology is the main development direction of society. This paper analyzes the application of artificial intelligence technology in physical training and predicts the future of artificial intelligence technology in the field of athletics to create a high-quality physical training approach.