bluuebunny/crossref_metadata_2025 huggingface dataset:
Systematic Literature Review of Smart Community Governance in China: Status, Challenges, and Opportunities
Authors: Wu Muhang
Published: April 2023

A lot of attention has been paid to smart communities, which are the final mile of smart cities in China, and pertinent research on smart community governance has been emerging. This paper examines the current situation, challenges, and development opportunities faced by smart communities based on systematic literature review (SLR), PEST model, and stakeholder approach in order to further highlight the direction of smart community governance in China and clarify the findings of previous studies. The current question, problems, and governance routes of smart community governance are presented in 77 important research publications that have been found after extensive searching, filtering, statistical analysis, and identification of all elements linked to smart community governance. Finally, based on the SLR framework, all of the influencing aspects of smart community governance are recognized, along with their internal relationships and interactions. The study also examines the problem of its growth from the perspectives of politics, economics, society, and technology (P, E, S, and T) (technology). The findings indicate that there are considerable inequities in the design of smart communities across nations and regions, that research on smart communities is still mostly focused on a single element and lacks quantitative analysis, and that overall smart community resilience is still low. To further advance smart development and resilient governance in China, academics must thoroughly analyze the elements impacting the creation of smart communities, incorporate them into future research, and improve the cooperation of numerous stakeholders.
Study on the Impact of Environmental Tax on Corporate Green Innovation under the Dual Carbon Target based on A-Share Mining Listed Companies
Authors: Ni Xiayun , Ma Mingming
Published: April 2023

Will the collection of environmental protection tax promote the green innovation of enterprises? Under the call of the 20th National Congress of the Communist Party of China to increase pollution prevention and ecological protection, this paper is based on Porter's hypothesis, taking China's Shanghai and Shenzhen A -share industrial listed companies from 2010 to 2020 as the research object, and empirically tested the impact of environmental protection tax on corporate green innovation. The research finds that: environmental protection tax significantly promotes green innovation of enterprises, and presents a hysteresis feature, that is, the current environmental tax significantly improves the level of green innovation of enterprises in the next period and the next period. After analysis, it is found that the role of environmental tax in promoting green innovation of enterprises is mainly reflected in state-owned enterprises, large-scale enterprises, and enterprises with low financing constraints. The research conclusions theoretically enrich the literature on the research on the economic consequences of environmental taxes and the research on the influencing factors of corporate green innovation, and provide important inspiration for improving China's environmental tax system and improving corporate green innovation in practice.
Examining the Consequences of Ericsson's Bribery Case in China by Discussing the Accountability System and Internal Control Measures
Authors: Yan Yilin
Published: April 2023

This article aims to explore the application of management control systems in business processes during Ericsson’s operation in China until 2016. It employs PEST Analysis and Porter’s Five Forces Model to conduct commercial analysis on Ericsson, and defines accountability in the context of the bribery incident, drawing from relevant literature. The research applies the Fraud Triangle as a basis to describe the circumstances surrounding the control failures and suggests two solutions that could have mitigated the failure. Finally, it evaluates the accountability provided by Ericsson in response to the control failure.
Analysis of the Real Exchange Rate of Vietnamese Dong from the Perspective of Demographic Dividend
Authors: Li Jiaqi
Published: April 2023

With the advantage of demographic dividend and extensive investment value, Vietnam's foreign trade has developed rapidly and made great contribution to its economic development. By studying the mechanism of the indirect effect of the population age structure on the promoting effect of economic development on the real exchange rate of the Vietnamese dong, it is of great significance to understand the long-term change law of the exchange rate of Vietnam and even other developing countries. In this paper, the influence of population age structure on the real exchange rate of VND(Vietnamese dong) against USD(US dollar) was studied. In accordance with the idea of "relevant theoretical analysis - empirical analysis - conclusions and suggestions", the unit root test, analysis of variance and other econometric methods were used for empirical test. It is found that there is a long-term equilibrium relationship between the population age structure and the real exchange rate of VND. It mainly affects the real exchange rate of Vietnam through the mechanism of economic demand, while the influence of "Balassa-Samuelson" on the supply side is weak. In the process of boosting economic growth, the age structure of the population leads to the depreciation tendency of the dong exchange rate. Based on the conclusions of this paper, relevant suggestions are put forward for Vietnam and other countries enjoying demographic dividend to improve the exchange rate formation mechanism, tap the second demographic dividend and develop demographic quality dividend.
Research on Human Resource Allocation Pattern and its Function -- Taking Google as an Example
Authors: Zhu Zeying
Published: April 2023

In the context of the global epidemic, the economic situation is grim and the economic crisis is rife. In order to better cope with the challenges and enhance their competitiveness, enterprises adjust their human resource allocation mode, improve work quality and drive their development. For social and economic management, human resource allocation is to fully and reasonably apply all human resources in the society to social production and economic activities. According to the current economic situation, the importance of human resource allocation has become increasingly prominent. This paper expounds the human resources allocation mode and its function, summarizes the problems and shortcomings, this paper takes the human resources allocation mode as the main research object, optimize the human resources allocation mode.
A Comparison of Three Asset-pricing Models During COVID-19
Authors: Liu Yuxuan
Published: April 2023

With the shock of COVID-19 pandemic bringing changes to world economy, it is necessary to reexamine the efficiency of models for asset pricing. In this paper, three different financial assets models, namely the CAPM as well as Fama-French (FF) three factor and five factor model, have been applied to provide explanation for the return of pharmaceutical industry of the US during the COVID-19 pandemic. According to the analysis, it is congruent with existing theories suggesting greater explanation capacity of the FF models over the CAPM and in this case, the FF five factor model is superior to the three factor one. However, the FF models are more complex than the CAPM and it takes more time and effort to conduct the calculations. It provides evidence that despite the restructuring of world economy the three models still stand and the FF5 model has the best explanatory power out of the three. These results shed light on guiding further exploration of financial asset-pricing models application in specific times.
Analysis of the Global Energy Strategy Game in the Post-Epidemic Era: Finding Evidence in Russia's Energy Export Policy
Authors: Gong Zheng
Published: April 2023

Since the globalization of the economy, energy cooperation between countries has achieved excellent results, with Russia meeting the energy import needs of its neighbors, especially the EU countries, with its abundant hydrocarbon supplies. However, as the productivity of countries increases, the uneven distribution of energy around the world has created a tighter energy supply situation and the need for security of energy supply is growing in the EU. At the same time, Russia and Ukraine have been involved in a number of conflicts over geopolitical and ideological issues, which could lead to the risk of disruption or reduction of energy supplies to end-consuming countries around the world. Although many scholars have studied Russia's energy cooperation with other countries, the process of international cooperation is fluid and therefore this paper will continue to analyse Russia's energy export policy for 2022, analyze the main drivers and impediments to Russia's energy cooperation with the European Union, and provide an outlook on future cooperation. The paper will therefore continue to analyse Russia's energy export policy in 2022, analyse the main drivers and impediments of energy cooperation between Russia and the EU, and provide an outlook on future cooperation.
The Impact of Financing Constraints on the Shadow Banking of Non-Financial Firms
Authors: Song Linyun
Published: April 2023

According to China's Financial Stability Report, since 2020, instability and uncertainty at both domestic and international levels have risen significantly. While financial risks are multifaceted, regional financial risks remain hidden, the risk of default on some enterprises' debts has increased, and the risks of individual small and medium-sized banks are more prominent. In the face of risks, China clearly pointed out that financial security is an important part of economic security. While promoting economic and social development, China should maintain the continuity, stability, and sustainability of macro financial policies, prevent financial risks, and deepen the reform of financial institutions. Currently, some non-financial enterprises in China are engaged in shadow banking through direct or indirect means. However, shadow banking has the characteristics of hidden risks and cross-market risk contagion, which can induce risks in the process of development and threaten the stability of the financial system and financial security. Therefore, further research on shadow banking is needed. Using a sample of A-share non-financial listed companies in China from 2010-2021, this paper conducted a fixed-effects regression on panel data to empirically investigate the regional heterogeneity of financing constraints on the shadow banking of non-financial enterprises and the heterogeneity of the nature of corporate ownership, and obtained the following conclusions. a) financing constraints will enhance the scale of shadow banking of non-financial enterprises. b) there is regional heterogeneity concerning the influence of financing constraints on the shadow banking of non-financial enterprises. c) there is heterogeneity in the nature of ownership for financing constraints.
Microsoft's Acquisition of Activision Blizzard: A Case Study
Authors: Hu Jie , Wu Chenyuan
Published: April 2023

On January 18, 2022, Microsoft announced its intention to initiate the acquisition of Activision Blizzard. It will pay for about $69 billion in an all-cash deal and the price will place a 45% premium on Activision Blizzard's value. Based on industry prospect analysis, competitor analysis, and SWOT analysis, this paper studies the case of Microsoft acquiring Activision to analyze the potential benefits and risks of this typical acquisition behavior. This paper finds out that the two company has a positive outlook since the COVID-19 pandemic pushed cloud service and the gaming market. The industry analysis as well as the SWOT analysis of both companies also indicate that they have the opportunity to develop mutually and complement each other's weaknesses. This paper may help to understand the business purpose, risks, and benefits of the acquisition from the perspective of future cloud services market development. On the basis of the key findings, this paper believes that the acquisition will contribute more positive effects to both Microsoft and Activision Blizzard.
Potential Benefits and Risks of Byte Dance’s Acquisition of Moonton: A Case Study Based on SWOT Analysis
Authors: Chen Yifan
Published: April 2023

In March 2021, ByteDance successfully acquired Moonton Technology and continued to expand ByteDance’s game industry. This article aims to investigate why ByteDance chose Moonton Technology among many overseas game companies, how the two parties completed the acquisition successfully, as well as the benefits that can be brought to both parties after the acquisition and the post-acquisition management model, and analyze how Moonton Technology can gain a leading edge in the competition with Tencent or what strategies and prominent games should be adopted. Based on the SWOT analysis method, this article explains the advantages, disadvantages, opportunities, and threats of Byte's acquisition of Moonton Technology, so as to make a correct analysis and future planning for this investor and manager. This article creates a new perspective for investors to understand ByteDance and Moonton Technology. This article suggests that investors should understand the background of the acquired company and whether it can produce synergies before investing, and consider long-term future planning.
Baijiu Industry Analysis Based on Various Indicators: Kweichow Maotai, Luzhou Laojiao & Shanxi Fenjiu
Authors: Chen Rongyu
Published: April 2023

While the Chinese stock market has been in a slump due to the COVID-19 pandemic, the profitability of Baijiu companies has been attracting attention. This article analyzes the financial data of three Baijiu enterprises based on three types of indicators, i.e., solvency indicators, profitability indicators, operating capacity indicators. To be specific, for solvency indicators, current ratio and quick ratio are utilized; for profitability indicators, return on assets (ROA) and return on equity (ROE) are utilized; for operating capacity indicators, inventory turnover ratio and accounts receivable turnover ratio are utilized. According to the data analysis, Kweichow Moutai maintains a leading edge and leads the development of the industry. At the same time, the profitability of Luzhou Laojiao and Shanxi Fenjiu increased steadily year by year. Hence, the future development potential is strong and this study claims that the Baijiu industry stock future trend is good, worthy of investment. This article puts forward feasible suggestions according to the shortcomings of each enterprise, and provides investment suggestions for shareholders. Overall, these results shed light on guiding further exploration of Baijiu industry, showing the growth potentiality for each company as well as the investment suggestion for the stockholders.
Based on financing efficiency evaluation and influencing factors of new energy enterprises based on DEA-Malmquist-Tobit model
Authors: Gu Maoqi
Published: April 2023

Based on panel data of 120 new energy enterprises from 2016 to 2021, this paper studies the current situation of financing efficiency of new energy enterprises. DEA-BCC model, Malmquist index and Tobit model are used to measure the financing efficiency of additional energy enterprises and analyze its influencing factors. The results show that the proportion of new energy enterprises with effective financing does not exceed 20 %, but the proportion shows a fluctuating upward trend ; the financing efficiency of new energy enterprises decreased by 2.6 %, which was more obviously restricted by the technological progress index. The level of economic development, the quality of workers and the level of technology have a promoting effect on the financing efficiency of different energy enterprises, while the asset-liability ratio, ownership concentration and operating cost ratio have an inhibitory effect.
The Influence and Mitigation of Consumer’s Price Sensitivity on the Pricing of Luxury Brands & Two Case Studies
Authors: Fu Jiayi
Published: April 2023

The current economic crisis and the worldwide Covid-19 pandemic, which is lasting about three years, have increased price sensitivity globally. A crisis-ridden external environment is a predominant explanation for this trend. However, what can be found is that previous studies have mainly relied on big data and have surveyed businesses as a whole, and thus, it has been unable to disentangle the price sensitivity in luxury brands. This report mainly uses the opinion of pioneers and collects the data from the official website of the luxury brands to figure out the current consumer’s preferences and their price sensitivity so that there can be more objective opinions. What is more, the report introduces the factor, the effect of price sensitivity and the way to alleviate it. The finding indicates that demand, income level and customer loyalty would affect most and mentions such mitigations as dynamic pricing and building more loyal customers.
The Relationship between Investors' Sentiment and Stock Market Returns: Evidence from Margin Trading and the CSI 300 Index
Authors: Xie Qingmei
Published: April 2023

In recent years, both academia and business have been more interested in investors' sentiment. Margin trading's deployment is beneficial to development of China's stock trading system. Margin trading, to some extent, reflect changes in investors' sentiment. To lessen the impact of noisy investors on stock market volatility, it is crucial to investigate the relationship between these sentiment changes and stock market returns. In this paper, the link between investors' sentiment and CSI 300 index returns is examined. To quantify investors' sentiment, the marginal trading variables eliminating macroeconomic factors are selected as proxy variables to research the correlation between investors' sentiment and CSI 300 index returns. The results show that investors' sentiment does not have a significant effect on returns. Stock market returns have a positive effect on investors' sentiment in the short run, but later this positive effect disappears quickly again. From the perspective of margin trading mechanism, this paper study the relationship between investors' sentiment and stock returns. The empirical results will provide a reference for small and medium investors to make rational decisions, and will also provide a scientific reference for improving the sentiment supervision mechanism of the stock market.
Preface: 2023 International Conference on Computer, Machine Learning and Artificial Intelligence (CMLAI 2023)
Authors: Katrine Evangeline , Li Jie
Published: April 2023

At present, Covid19 is still having an enormous impact in global areas. International or long-distance travels are still risky. In consideration of the safety of all speakers and participants, the 2023 International Conference on Computer, Machine Learning and Artificial Intelligence (CMLAI 2023) had been held online via Zoom during January 14-15, 2023 in San Francisco, USA.

CMLAI 2023 is an international forum devoted specifically to facilitate synergies in research and development in the areas of computing, machine learning and artificial intelligence. It provides the communication platform for those who are leading experts and scholars from around the world. The development of artificial intelligence and their corresponding technologies and industries has also being regarded as "national development strategy" for the world's leading countries and gradually developed into a new "trump card" in the game between countries.

This conference was attended by over 200 researchers, scholars, academic and industry experts from many countries. During the two-day online conference, 3 keynote speeches with topics on cloud computing, machine learning, and artificial intelligence were delivered. And 42 oral and invited presentations were presented and discussed in-depth in 3 technical sessions, with interactive communication among the attendees. Despite the inhibiting factor of Covid19, the conference successfully promoted exchanging new ideas and perspective discourse among the participants in their respective fields.

CMLAI Organizing Committees

San Francisco, USA
Study for Performance of Un-Pretrained and Pre-trained Models based on CNN
Authors: Wang Bingsen
Published: April 2023

In recent years, as the accuracy of deep learning algorithms in image classification tasks exceeds that of the human brain, Artificial Intelligence (AI) auxiliary diagnosis systems have attracted more and more attention. In this paper, some commonly used Convolutional Neural Network (CNN) models e.g. MobileNet, VGG and ResNet are trained and compared on the cancer detection dataset, and it is found that the pre-trained models based on the idea of the transfer learning perform better than the newly trained models in terms of training speed and model performance. Thus, it can be seen that the transfer learning method has great potential in the field of cancer diagnosis. This study provides some experimental support and suggestions on how to further improve the property of the transfer learning method in the field of cancer diagnosis. Meantime, the performance of VGG19 can be proved to be better compared to other models (i.e., MobileNet and ResNet).
Comparison based on Multiple Machine Learning Algorithms in the Case of Image Classification
Authors: Zhang Zehui
Published: April 2023

With the upsurge of usage of the internet, the society has turned into a Big Data Era. Living in the world overwhelmed with data, the image is among the most common forms. Utilizing these images smartly is the big topic. Image Classification refers to the process of extracting information classes from a raster image that consists of multiple bands by analyzing the raster image. It is the basis for lots of applications, like radar and autopilot. Among tremendous channels for dealing with image, deep learning, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), is always the first thing flashing in peoples’ mind. In this paper, studies will be implemented and discuss the way to perform image classification utilizing machine learning algorithms. Four algorithms will be implemented, namely Random Forest, KNN, Decision Tree, and Naive Bayes. And at the end of the paper, this result shows that Random Forest Classifier has the best performance compared with the rest of the three algorisms. But it is still far away from the requirement for daily application use like radar. Poor performance and the long operation time make machine learning algorisms out of date when doing image classification. By this comparison, deep learning replaced machine learning and becomes the first choice do this task.
Pathological Images Classification based on the Pretrained Convolutional Neural Network
Authors: Wang Qifeng
Published: April 2023

Convolutional neural network is more and more used to solve the problem of images classification, but the scale of the dataset in this filed is too small compared with that of natural image recognition, which leads to the slow convergence speed of the neural network, and the instability of the model, which is difficult to be used to predict the results. In this paper, the residual network is applied to the PCam dataset to realize the recognition of tumour cells, and pre-trained through the ImageNet dataset. In the same training epochs, the performance of the pre-train network is much higher than that of the randomly initialized network, which significantly improves the convergence speed of the neural network. In addition, the performance of the pre-train network can converge faster when the amount of data is sufficient, and in the case oof medical image recognition with less data, the performance of the pre-train network is much higher than that of the random initialization network.
Investigation and Development Prospect of Hand Exoskeleton Comfort
Authors: Bian Jingyu
Published: April 2023

Stroke is a major problem in medical health, patients will have a lot of sequelae after cure. The purpose of this article is to review the results of previous articles and experiments on the comfort of different hand exoskeletons, aiming to improve the comfort of users. We analyzed the materials, wearing methods and binding methods of skin contact parts of various exoskeletons, and searched the corresponding keywords in Google Academic website to find the appropriate articles. Then the different ways of wearing exoskeletons were compared according to the test results. Then the comfort of each exoskeleton is concluded. The goal is to better explain and summarize the comfort of today's hand exoskeletons. Finally, the comfort characteristics of different methods are obtained according to several papers. Comfort is very important for patients, and the comfort of flexible exoskeletons is better than that of rigid exoskeletons.
Effect of VR on Video Games Story Telling
Authors: Wang Sanxing
Published: April 2023

Now days, more and more virtual reality games appear on market, many of them present with strong storytelling elements. The narrative type of video games in the story development, character shaping, let the audience resonate. However, the articles about story telling in virtual reality games is not enough. This article has concluded about what is storytelling and what makes it effective to players experience; emergent storytelling, immersion and environmental storytelling has been took out and discussed in detail, Paper also discussed about what virtual reality have changed video games and how these changes affect storytelling in video games. Main feature of virtual reality like Interactivity and locomotion tracking been discussed. Different storytelling had been evaluated under virtual reality background. Especially, at the end of the article, this paper summarizes the full text and prospects the related content.
Emotion Recognition of Music based on Machine Learning Scenarios
Authors: Li Zenan
Published: April 2023

As the rapid development of computer music, the technique for recognizing the emotion of music also have many progresses. After the brief introduction of the history of computer music, this paper mainly discusses about the current existing machine learning models for the emotion recognition in music. The complexity of emotion has been emphasized in this paper for several reasons. In addition, by comparison different models, this paper summarizes common features, metrics and steps used in music emotion analyzation. Moreover, this study finds out the limitations and disadvantages of different classifications and feature extracting method for different models, pointing out the living problems, e.g., the difficulty of emotion recognition for experimental music. To sum up, this paper summarizes and analyzes the primary studying in the field of music emotion recognition, offering a guideline for implementations of different machine learning approaches in the field. These results shed light on paving a path for further exploration of emotion recognition in computer music.
Lung Cancer Risk Analysis and Prediction Using Machine Learning Techniques
Authors: Ding Hongyi , Tong Qi , Wang Hongran , Zheng Zhan
Published: April 2023

In this work, the main challenges are to find the factors for lung cancer and to use machine learning techniques to analyze the risk of lung cancer. Lung cancer is a malignant tumor, usually arising from the bronchial mucosa or glands of the lungs. The death rate of patients is very rapid. The incidence and death rates of lung cancer are increasing year by year in many countries. Over the past 50 years, many countries have reported significant increases in lung cancer morbidity and mortality. The incidence and mortality of lung cancer in men rank first among all malignant tumors, and the incidence and mortality in women rank second. The random forest and logistic regression are used to predict lung cancer risk based on patients' symptomatic and behavioral features.
Prediction of COVID-19 Pandemic Trend by Machine Learning
Authors: Xu Shijia
Published: April 2023

Since 2020, COVID-19 has had a huge impact on people's lives. Including but not limited to economic, educational, medical, and other aspects. During this period, all sectors of society and the government have intervened reasonably, so it is necessary to analyze the data on COVID-19 so far and make scientific predictions. This article starts with the analysis of raw data on COVID-19 from the World Health Organization (WHO). Then four machine learning methods, including the time series model, exponential smoothing model, XGBRegressor method, and polynomial regression model, are leveraged for trend prediction of the original data. The data, with the time ranging from January 2020 to May 2021, is taken as the training object, and then the epidemic in Jul 2021 is used for testing. The number of cases is predicted and compared with real data. It is concluded that the WHO has indeed carried out effective intervention in areas seriously affected by the epidemic and that the time series model predicts the minimum loss value.
Monkeypox Cases Prediction with Machine Learning
Authors: Wang Anqi , Li Dongxiao , Shen Wei , Zhang Xianghan
Published: April 2023

In May 2022, the monkeypox epidemic broke out worldwide. According to the cases collected by the Centers for Disease Control and Prevention (CDC), as of August 17, 2022, 13,517 cases were confirmed in the United States. Based on the 2022 U.S. monkeypox epidemic data collected by the CDC, the Prophet prediction model released by Facebook in 2017 was used to predict the future trend of the monkeypox epidemic in the United States. The range of daily confirmed cases for the 2022 U.S. Monkeypox epidemic applied from May 17, 2022, to August 10, 2022, with reporting date. The results forecast monkeypox trends in American monkeypox over periods as short as seven days and as long as 17 days, with all values of MAPE in this prediction having good forecasting accuracy between 0.018 and 0.117. The prediction model’s accuracy can be affected by the k-value biasing due to strange events and the implementation of epidemic prevention policies.
Music Generator Applying Markov Chain and Lagrange Interpolation
Authors: Xu Yurui
Published: April 2023

Algorithmic composition is the process that composes music based on algorithms by formulating specific rules that incorporate relevant music arranging theory and assigning these rules to a suitable computer algorithm. One common approach is to use Markov models to generate note sequences. However, the generated notes do not consider the overall chord progression and the rhythmic pattern of the section, which is not effective when dealing with long multi-track music, especially for more structured works, e.g., classical and pop music. Therefore, this paper introduces an improved approach that uses Markov chains for chord generation, and then uses Lagrange Interpolation to generate melodies and accompaniments for multitrack customized instruments using chord progression notes as anchor points. In this study, only 30 common chord progressions and 30 rhythmic progressions were used as the input data to generate a sample music with cat sounds as the main melody, violins as the sub-melody, and pianos as the accompaniment. It eliminates the mentioned problems associated with the adoption of Markov chains for melodic composition. In this case, it allows for the creation of tonal music with an overall structural, multi-track approach, by using chord progressions as a guide to the melody and even the accompaniment, and by controlling the structural characteristics of the music through rhythmic patterns.
AI Color Organ: Piano Music Visualization using Onset Detection and HistoGAN
Authors: Xu Shu
Published: April 2023

The music visualization algorithm described in this study allows users to construct piano audio files using imported image files. This paper contributes to previous studies and designs of sonification by highlighting the effectiveness of utilizing onset detection in creating intuitive sonic changes. The audio-visual correspondences employed in this study could be expanded to many other syntheses and sample manipulation techniques. Translating visual information into sonic changes could yield many creative applications in music production, as it offers musicians a simultaneously optical and auditory production experience. This approach to audio manipulation also increases the unpredictability of the sound output, which could be appealing to experimental musicians seeking to control sounds with the visual structure of artworks that they enjoy, as opposed to precise parameters. It is looking forward to seeing creative implementations of the techniques in audio-visual artworks, music production tools, and interactive multimedia systems. These results shed light on guiding further exploration of AI composing.
Researches Advanced in Generative Adversarial Networks and Their Applications for Image-Generating NFT
Authors: Guo Xiaolin
Published: April 2023

A generative adversarial network is a deep learning model, an unsupervised learning method. In computer vision, the generative adversarial network is a research direction with rapid development in recent years; Similarly, the rise of cryptocurrency Non-Fungible Tokens (NFT) in recent years has also attracted much attention to the field of art. As an "irreplaceable currency" NFT provides a more novel and convenient way for content creators and artists to create and increases the continuous income of original creators. At the same time, it has also attracted widespread attention to the financial field. Therefore, this paper is determined to combine the generative adversarial network of the production of NFT and discuss and analyze the autonomous computer generation of artworks. Firstly, this paper starts with the model's structure, the design of the objective function, Block chain technology, and Irreplaceable tokens encrypted using blockchain technology. Then, the image generated by the whole generative adversarial network and transformed into NFT works are described in detail. In addition, this paper briefly discusses the development ethics of human art and machine art and the prospects for its development trend.
Glass Classification and Identification based on Decision Tree and Random Forest Classification Models
Authors: Zhang Zhipeng , Wang Tong , Wang Xinpeng
Published: April 2023

Glass artifacts are weathered due to age and improper preservation, etc. This paper analyzes the classification laws of high potassium glass and lead-barium glass based on the data; for each category, suitable chemical compositions are selected to classify them into subcategories, and firstly, a decision tree classification model is used to analyze the classification laws of the two categories of glass by substituting the data set for machine learning, and specific classification methods and classification results are given. And the chemical composition of the unknown category of glass artifacts is analyzed, and the random forest classification model is used to predict the training set and test set by.
Difference of Brain Electrical Activity Mappings in Sleep Stages
Authors: Liu Tianyi , Shen Mingshen , Wang Xiaohan
Published: April 2023

According to a survey by the World Health Organization, the proportion of people who has difficulty in sleeping is up to 27%. Detecting the cause of these sleep disorders needs an elaborate analysis of the physiological signals of different sleep stages. Analyzing and comparing the brain electrical activity mapping energy difference of normal subjects and subjects who have the disease of nocturnal frontal lobe epilepsy is introduced in this study. The brain electrical activity mapping is from the independent component analysis (ICA) of the Electroencephalograph (EEG) waveform. The EEG data set is coming from the CAP sleep database. The control group uses the data of n3, n10, and n11. The experimental group uses the data of nfle1, nfle2, and nfle3. The EEGLAB, a toolbox in MATLAB, is used to preprocess the EEG waveform and locate the area where signals are generated in the brain. The preprocessing steps include channel locations, selecting data, filtering, re-referencing the data, ICA, and artifact rejection. After the preprocessing, there are 13 electrodes retained and the energy difference of the brain electrical activity mapping will be compared between the control group and the experimental group by observation.
Modeling Temporal Correlation for Prediction of Wind Power
Authors: Dong Zeyu
Published: April 2023

The use of clean energy increase in the worldwide with an increasing number of people realizing the energy and environmental problems. Wind power is one of most likely to be widely used power, which can produce a huge amount of power by wind power stations. This paper used long short term memory (LSTM) to calculate the wind power curve. By analyzing the principle of the algorithms and establish the model to predict the real data, the feasibility of the model was proved. Furthermore, extensive comparison experiments are conducted to demonstrate the LSTM model has a highest accuracy and efficiency. In summary, the model can be used for the wind power curve forecast system.
Embedded Implementation and Evaluation of Deep Neural Network of Federated Learning
Authors: Zhao Zhuoyue , Wu Feiyu , Dong Chao , Qu Yuben
Published: April 2023

Compared with traditional distributed machine learning, federated learning (or joint learning) enables multiple computing nodes to cooperate and train a shared machine learning model without transmitting original data. At present, the research work of federated learning mainly focuses on the theoretical method, and the system implementation is less, and only for the text data or simple image such as medical institution information sharing, handwriting font recognition and other simple neural network applications. Aiming at more complex deep neural networks, this project implements a multi-node federated learning system on embedded device, and evaluates its key performance indicators such as training accuracy, delay and loss. The research method mainly uses embedded computer both as client and server, adjusts and groups the Visdrone datasets as training samples, and then trains the model on the client based on YOLOv4 algorithm, realizes the encrypted transmission of information through TCP protocol, and achieves the aggregation update of the model on the server with FedAvg algorithm.
Dynamic Asset Planning Model via Polynomial - ARIMA Prediction and Risk Analysis via Improved Volatility & EWAM
Authors: Zeng Fanwei , Wang Yulu , Yang Xinhao
Published: April 2023

This paper investigates the trading strategies for gold and bitcoin. Based on past data, future gold and bitcoin prices are given by single-step forecasting, and optimal investment strategies are also proposed. In this paper, a polynomial-ARIMA model is developed using quadratic polynomial fitting and ARIMA algorithm depending on the size of the data. This forecasting model was used for single-step forecasting of gold and bitcoin prices. After that, a GA-based dynamic programming model was proposed based on the forecasting results and the results were obtained by it.
Design and Implementation of an Executive Search Customer Relationship Management System
Authors: Liu Xiaohan
Published: April 2023

An executive search company seeks and recruits’ talents for client organizations. Therefore, it is essential for an executive search company to establish well-maintained relationships with their client and candidate talents. A Customer Relationship Management (CRM) system helps with the task of maintaining customer relationships. The underlying database is a crucial component of a CRM system because the database is responsible for storing the information of the clients and other data associated to the executive search tasks. This paper designs a database specifically for the scenario and requirements of an executive search company, according to the daily operations that the company needs to carry out. This paper designs the entities and relationships between entities based on the executive search requirements. A relational model of the database design is also included in this paper. This paper implements the database in SQL. Finally, this paper implements a user interface to manage and maintain the database of the CRM system.
Game Designs based on Machine Learning Systems
Authors: Chen Yichong
Published: April 2023

Contemporarily, there are many games as follow a storyline in the game industry. On this basis, NPCs are acting under a setup. In this paper, several machine learning systems are creating games and art pieces by themselves. Readers can see how different machine learning system works to form different games. Based on the procedure and evaluations, it will help a lot of game developers to create a full AI system for creating different levels, NPC, monsters, and missions. Therefore, it will create a world that is even more freedom for players to play. ANGELINA system and those two art systems based on machine learning they are creating some random production by themselves, and all the works are high quality. The purpose of this paper is to demonstrate the functions machine learning works in the field of game creation. These results shed light on guiding further exploration of game developing in terms of machine learning scenarios.
YOLO Series Target Detection Technology and Application
Authors: Zhang Yuan
Published: April 2023

Recently, YOLO is the most popular algorithm in machine learning. The algorithm has developed rapidly, and there are several versions at present. Each version of the framework is different, and they also have their own application areas. And maybe in one area, not only one version can be used. This paper summarizes the process of target detection, the structures of YOLO network. In addition, this work also analyzed the development, advantages and disadvantages of YOLO target detection. Finally, the application of YOLO in automatic driving and UAV detection are discussed. YOLO may develop faster in the future. YOLO model is a variable model, which has unique functions when detecting different things under different circumstances. At the end of the paper, the thesis is summarized, and the related research has certain reference value.
Concurrency and Operating Systems, Processors, and Programming Languages
Authors: Sun Bojin , Sun Minze
Published: April 2023

The main content of our research is to optimize and improve the concurrency of the system from the three aspects of operating system, processor and programming language. Previous solutions have focused on hardware techniques to improve concurrency. Our goal is to solve the problem of resource occupancy and contention through algorithms, data structures, optimization interrupts, and optimization of process and coroutine scheduling. In the final ideal case, the finite-state machine switching code generated by the compiler can be used to switch the coroutines in the same process. For threads, the thread code provided by the kernel does the switching. The asynchronous system call is optimized to automatically generate the corresponding system call request code according to the execution compiler and maintain the coroutine control block data structure. This is also the most difficult part of this research.
Design and Implementation of Game Guild System based on Spring Boot and WeChat Applet
Authors: Tian Rundong , Zhao Lingke
Published: April 2023

Nowadays, more and more children are exposed to all kinds of online games, which can bring different effects to children, both good and bad. Guild systems in some games are able to allow children to meet new people, realize the importance of teamwork and sociability, and improve their confidence and teamwork skills. Game guilds have become one of the most popular social choices for the younger generation. According to the current research, the game guild management system also has some functions that can be optimized, mainly reflected in the user's inconvenience. Therefore, we propose a reasonable and practical solution to this problem, which is to design a WeChat program to handle transactions anytime and anywhere. Based on the existing excellent cases and the analysis of various platforms and tools, this paper designs and implements a game guild management system based on Spring Boot framework, Spring Security framework and WeChat Applet. The system can serve three objects of guild: guild administrator, guild member and guild president. The main functions include member information management, association active management, reward information management, forum management and comment management. The system is easy to operation, to a certain extent to simplify the work of the guild administrator, and convenient for users and visitors.
Further Perspective of Machine Vision in Industrial Robot Systems
Authors: Zhu Yunxuan
Published: April 2023

With the rapid development of automation systems and artificial intelligence, industrial robots have played significant roles in automated production processes. Due to the improvement of computer chips, more and more vision algorithms are able to run in industrial robot systems. Increasingly, robotic systems based on visual recognition are replacing those based on traditional sensors. However, there is much work to be done in real situations. This research focuses on the application of machine vision in the field of industrial robot systems. It first gives the overall summary of industrial robot systems and machine vision as well as their applications. Then it gives an example of a sorting system and expounds its strategy. Finally, it turns out that machine vision can be widely used in industrial robot systems because of its excellent performance, although there are still existing problems to be solved in it. Those problems may be solved by further development of algorithms.
Comparative Analysis of Pix2Pix and CycleGAN for Image-to-Image Translation
Authors: Lin Eason
Published: April 2023

Image-to-Image translation technology is nowadays a prevailing research orientation in computer-vision domain, which aims to translate the styles and features in images from one image domain to another. With the rapid development of the convolutional neural networks, especially generative adversarial network technology, breakthroughs have been made in the performance of image translation, which has been widely used in many fields such as labeling photos to synthesize photos, reconstructing objects from line drawings, and coloring pictures. The Image-to-Image Translation problem is essentially a pixel-to-pixel mapping. Limited by specific task settings, the performance of different general frameworks often fluctuates greatly when dealing with different translation tasks. In this paper, focusing on the above problems, the advanced and commonly used Image-to-Image Translation frameworks such as Pix2Pix and CycleGAN, are selected to compare and analyze the advantages in detail from different dimensions including network structure, loss function, applications and model accuracy. Furthermore, for different practical application scenarios, we discuss solutions based on these two representative frameworks and show the results after image translation processing. Finally, the existing problems and future research directions are discussed and summarized.