Reviewerly/exHarmony huggingface dataset:
Green supply chain financial governance and technology application based on block chain and AI technology

Global warming and environmental protection will be the common challenges facing mankind in the 21st century. The sudden outbreak of COVID-19 in 2020 also makes us more deeply aware that all sectors of the world must strengthen their attention to the environment, society, and governance (ESG) to promote global sustainable development, The renewal of the green supply chain finance system assisted by science and technology will be a hot topic for new research or breakthrough for future. The author studied the application of green supply chain finance based on the method of combining blockchain and AI technology and proposed the idea of a green supply chain finance platform driven by blockchain technology and the Wide&amp;Deep algorithm. It is found that the combination of blockchain technology and the WIDE&amp;DEEP algorithm can better solve the lack of unified green standards and effective information disclosure and information sharing mechanisms in green supply chain finance. Small and medium-sized institutions financing difficulties, financing expensive, high cost, low turnover, ecological supply chain information flow, business flow, logistics, capital, etc in the "four circulations" a series of problems such as poor communication problems, blockchain algorithm combined with technology and AI can provide a decent future for supply chain finance development direction, how to practice is the direction of future research to its success.
Prediction of Ethereum Prices Using Linear Regression and Long Short-term Memory

Contemporarily, the popularity and use of cryptocurrencies has risen along with their prices and Ethereum is the second most popular and largest cryptocurrency after Bitcoin. Cryptocurrencies are based on the blockchain, which is a decentralized technology that has the power to change any banking system. They have become an attractive investment for both traders and individuals looking to invest. The price of Ethereum fluctuates and is affected by various factors, e.g., the crypto trading exchange as well as supply and demand. Ethereum is so valuable because it can be used as cash and one also pay Ethereum in full or in part to someone in exchange. Besides, it is easily guaranteed by the blockchain. Unlike stocks, the price of Ethereum is much more variable because it is traded 24 hours a day and there are no closing times. On this basis, this paper compares the results of two different models, namely linear regression and Long Short-Term Memory networks (LSTM). The dataset comprised in the closing prices of the last 372 days for Ethereum. The performance of the obtained models is critically evaluated using statistical indicators Root Mean Squared Error (RMSE) and the study have drawn our conclusions based on the RMSE result. The paper demonstrates a technique for using time series data in both models and determining each model's RMSE. These results shed light on guiding further exploration of prediction Ethereum prices and trends.
Price Prediction of Bitcoin Using LSTM Neural Network

Contemporarily, cryptocurrency has a high market value, and the price of cryptocurrency fluctuates dramatically. This article analyzes the parameters effects of the LSTM model on Bitcoin price prediction accuracy based on Python and modules of Numpy, Pandas, Keras, Tensorflow, and Sklern. The analysis clarifies the relationship between the accuracy of Bitcoin price prediction and different parameters in the LSTM model. It is discovered that when larger batch sizes are supplied at minor epochs, the accuracy of Bitcoin price prediction declines. Meanwhile, the number of neurons affects the accuracy. In addition, compared to lengths of 14, 30, and 60, the prediction error grows greater when a single time sequence is 7 in length. Apart from that, at present, using closing prices from the past two years rather than the past 1 year, 3 years, or 5 years can make predictions more accurate. These findings shed light on recommendations for adjusting various parameters in the development of the LSTM model for Bitcoin price prediction.
Design and Conception of Will Notarization System Based on Ethereum

It is crucial to use blockchain to notarization system. Although a notarized will has the highest legal effect, the notarization process is cumbersome, the notary agency has heavy responsibilities and high costs, which has become a drawback of notarized wills, and which makes many people give up notarized wills. This research will design a will notarization system based on blockchain technology to solve the problem of difficult will notarization. Through demand analysis and scenario analysis, this paper builds the overall architecture of the will notarization system based on blockchain technology and the Ethereum platform. The architecture consists of three parts: the client used by the user, the server provided by the notary office, and the blockchain module. Based on the Truffle framework provided by Ethereum, it will involve multiple links such as HTML front-end development, JavaScript development, solidity smart contract development, Ethereum interface call, network construction, etc., to design the entire system deployment and operation process.
Credit Card Fraud Detection Using Predictive Model

Credit card is a sign of credit that is given to customers with good credit by a commercial bank or credit card firm. It takes the shape of a card with signature blank space on the back and the name of the dissipated bank, expiration date, CVS number, and cardholder name on the front. A credit card is a payment card that can give the cardholders’ abilities to enable the cardholder to exchange for goods and services based on their credibility and debt score. In this paper, it will explore the credit card fraud detection predictive model to avoid fraudulent activity. Through different algorithms, the study could easily show the potentially fraudulent activities in the given dataset. In order to effectively combat credit card fraud, a number of techniques have been developed and put into practice, including different supervised and unsupervised machine learning algorithms to predict fraudulent activities. These techniques will be used to compare between the actual dataset and estimated models to illustrate the full picture. The credit card fraud is a challenging problem, especially it is prevalent during college students.
Improved credit card fraud detection method based on XGBoost algorithm

With the development of the Internet and technology, credit cards are more widely used and transaction data are larger. The data set of credit card fraud is a typical imbalanced data problem. The model should ensure that fraud is detected and customer service quality is guaranteed. Improving both the precision and the recall rate is the focus of current research. However, when the precision is constrained by the current level of machine learning, it is a good choice to use different models to evaluate and obtain more data for manual use. Few papers use the results of both models for comprehensive judgment. In this paper, two sampling methods (undersampling-NearMiss, oversampling-SMOTE) and three algorithms (Logistic, Neural Network, XGBoost) are used to analyze data based on the transaction records in Europe within two days in September 2013. The above six cases were compared to explore appropriate detection methods. The study found that Nearmiss-XGBoost had the best recall rate, SMOTE -XGBoost had the best comprehensive result and precision. Compounding the results of the two models can improve the precision and recall.
Trading Strategies for Cryptocurrencies Based on Machine Learning Scenarios

A Cryptocurrency is a peer-to-peer digital exchange system in which cryptography is used to generate and distribute currency units. Bitcoin as the foremost digital currency, using asymmetric cryptographic algorithms, blockchain technology, was conceptualized by Satoshi Nakamoto in 2008 and born in 2009. In 14 years, digital currency has gone from being initially controversial and worthless to rapid increase in value. The huge fluctuations in its price have attracted worldwide attention, and more people have begun to pay attention to the investment strategy of digital currency. Starting from the attributes of Bitcoin, this paper objectively compares the application effect of arbitrage strategy and trend strategy in machine learning on Bitcoin, analyzes and summarizes and predicts the future of Bitcoin's investment. To be specific, the arbitrage strategy involves three methods, i. e. , cash arbitrage, cross-exchange arbitrage and related variety arbitrage; trend strategy involves two methods, i. e. , the timing method and the multi-factor method. These results shed light on guiding further exploration of potential of investing digital currencies, which provides an in-depth summary analysis of risk-free arbitrage and digital currency value forecasts.
BTC, ETH and Dogecoin Price Prediction Based on OLS, Random Forest and XGBoost

In recent years, the digital world is fast speeding developed from decentralised concept to blockchain, then to cryptocurrency. Especially, cryptocurrency is a popular trending in recent decades that attracts different experts from various field. Its high volatility has been attracted plenty of investors while also brings the difficulty for realizing the price forecasting. On this basis, this study uses public cryptocurrency dataset and three analytical models to predict the direction of cryptocurrency’s price. To be specific, three underlying assets covering large proportion in cryptocurrency are selected, i.e., Bitcoin, Ethereum and Dogecoin. According to the analysis, the prediction results of different models and approaches will be presented. At the end of study, it gains that the optional model with appropriate hyperparameters based on the judgement of metrics values, which offers relevant suggestions for future works. These results shed light on guiding further exploration of cryptocurrency price prediction in terms the state-of-art machine learning scenarios.
Predicting the Price of Bitcoin, Dogecoin and Ethereum by Machine Learning

Contemporarily, blockchains and cryptocurrencies have gained their popularity among investors and hedge funder, where both have bright prospects. On this basis, cryptocurrencies have been used in trading more and more with the development of website and computer. In this case, their prices fluctuations do have great significance to the public. This paper chooses three machine learning model (i.e., XGBoost, LightGBM and Linear Model) to predict the price of three cryptocurrencies (i.e., Bitcoin, Dogecoin and Ethereum). To be specific, this study uses the data from 2020-01-01 to 2022-12-07, including close price, open price, high price, low price, and the volume of trading coins. According to the analysis, Linear Model can predict the price best, with well-fitted trend prediction and accurate price prediction. In addition, other models can also have good predictions but they are not better than Linear model. These results can help others to predict the price of cryptocurrencies and have a deep understanding of cryptocurrency and machine learning.
The Impact of Fake Reviews of Online Goods on Consumers

The fake product reviews on the Internet have brought great obstacles to consumers to make the right purchase decision. Fake reviews reduce the goodwill of e-commerce platforms and harm the interests of most merchants on the platforms. The appearance of fake reviews is easy to mislead consumers to make wrong decisions. Therefore, the research and identification of fake comments are urgent and significant. This paper will detailed discussion and analyze the impact of fake reviews on consumers from the perspective of the formation of consumers' purchase decisions. The four dimensions are demand cognition, looking for alternative plans, purchase decisions, and purchase behavior. Fake reviews may stimulate consumers' purchase desire by changing their demand perception. When evaluating alternative plans, they are affected by the reputation of merchants, etc., and fake reviews significantly affect the purchase decision. Consumers may make positive or negative comments after a purchase. When reviewers do not have expectations for the goods they receive, they become distrustful of the business and the platform, and they give emotionally negative reviews. If satisfied, positive publicity feedback will be given. This paper conducts literature and case studies on the impact of poor information on consumers caused by artificial evaluations and the prevention and control of fake reviews. The author will analyze the reasons for the occurrence of false comments and discuss how to prevent the occurrence of false information to the maximum extent, including the establishment of reward and punishment mechanisms, innovating the detection technology of fake commodity reviews to avoid unfair competition, and strengthening the control of real-name information on the Internet.
Application of Kano Model in Risk Identification of Software Development Projects

Risk management and identification of software projects is an important factor in software project management. Software project risks will affect the implementation of the project plans, and if they do, they will affect the project progress, increase the project costs, and even make the software project unfeasible. Risk occurrence can be minimized if risk management and identification are implemented in a project. However, in China, many software development companies do not have the awareness of risk management and identification, or use inappropriate management technology, resulting in software development projects are often delayed, beyond the budget or even failed. Successful project management requires good risk management and identification of projects. This study adopts the Ko model to scientifically and reasonably determine the priority of risk management of software projects, and take corresponding countermeasures for different risk factors, to ensure the smooth implementation of software projects, and then ensure the completion of them and the realization of objectives. This ensures the successful completion of software projects and optimal management with limited resources.
Scientific Information Analysis Using Text Analysis Tool “Voyant Tools”

This article describes the use of “Voyant Tools”, an open access text analysis application, to examine a corpus of articles from open access journals, dealing with the topic of digital humanities. The corpus consisted of 404 articles recorded in the “Clarivate Analytics Web of Science” and “Scopus ScienceDirect” databases. The authors discuss how “Voyant Tools” aids to identify the dominant fields of research through quantitative methods and to reveal the main discourse themes using distant reading and interactive reading capabilities. They also identify some problems encountered during the analyses, and also discuss the usefulness of data visualization for research and interpretation. Computer tools can be useful for experienced researchers who are interested in quantitative text analysis, as well as for beginners, as it provides an opportunity to acquire basic knowledge that will lead to a deeper interest in textual analysis methods.
Analysis of The Problem of Low Sales of Insta360 Panoramic Cameras

The Insta 360 panoramic camera simulates a two-dimensional floor plan into real three-dimensional space. Panoramic landscapes diversify the way an image is viewed, and at the same time, add interest. In addition to good interactive performance, the sense of realism is also very strong. Most importantly, objects can be seen in perspective, also known as naked-eye 3D. The purpose of this study was to explore why Insta 360's product is new and unique, and why sales are so low that the company has faced two innovation failures. After further analyzing its technical conditions, market atmosphere, and application scenarios, this paper improved product performance and development direction. This study concluded that only the novelty of the product is considered, ignoring immature technologies and markets and limited application scenarios. These led to low sales of the product in the first two times. Therefore, the panoramic camera is an emerging product, and it is necessary to determine the target user. Then, discuss the development model suitable for the Insta 360 panoramic camera under the above conditions.
Improving the Prediction Accuracy in Deep Learning-based Cryptocurrency Price Prediction

Cryptocurrencies are popular today even though they do not have a physical form with their high profit rates and increasing usage day by day. However, the volatility of cryptocurrencies is higher than physical currencies. These volatilities change with the effect of social media rather than changes in exchange rates of physical currencies. For this reason, in this study, using Twitter data, one of the most widely used social media tools, real-time analysis on the values of four cryptocurrencies with the highest market value and the change in the estimated success compared to classical approaches were examined. The basic steps of this study: Obtaining Twitter data and financial data, performing sentiment analysis using Twitter data, making predictions on MM-LSTM architecture. The approach is aimed to be a predictive method open to online learning. Various filter steps were applied to remove the effect of bot users on Twitter that could prevent the prediction performance on the created data set, and the effect of the method on accuracy rate was tried to be reduced by eliminating the activity of bot accounts.
A generalized decoding method for neural text generation

In natural language generation, most decoding methods are not intrinsic because their performance depends on extrinsically configured hyperparameters. It means that: first, the generation system is dynamic under different conditions while the decoding system is always static under any conditions once its hyperparameters are extrinsically fixed; second, it is hard to select a constant decoding hyperparameter that is omnipotent for all conditions. Although there are decoding methods that are hyperparameter-free, such as greedy and plain sampling, it has been well studied that these methods generally perform worse than methods with hyperparameters, such as beam search, top-k and top-p. Decoding with hyperparameters can get infinite strategies from different fixed configurations, while hyperparameter-free methods have only one strategy. Therefore, the comparison between them is actually unfair, which is a one-vs-infinite battle. So how to deal with the decoding hyperparameters properly and intrinsically? Is it true that hyperparameter-free methods are always inferior to methods with inexhaustible hyperparameter configurations? Is it possible to design a generalized framework, by which these decoding methods can be naturally connected, uniformly described, and mutually inspired? In these paper, we try to find answers to these questions. To this end, we first propose a generalized decoding framework, i.e., GSD, that can be used to uniformly describe and connect existing popular decoding methods. As far as we know, this is the first work trying to build a theoretical framework to associate these decoding methods in formal mathematical theorems. Based on the framework, we then propose Intrinsic Decoding, a novel implementation of GSD with distinctive design from existing decoding algorithms: it is intrinsic and dynamic. Intrinsic Decoding changes the aforementioned comparison from one-vs-infinite to dynamic-vs-infinite. Just like greedy and sampling, Intrinsic Decoding has no hyperparameter, while effecting better than both greedy and sampling, even achieving comparable performance to the methods equipped with inexhaustible hyperparameter configurations, such as beam search, top-k and top-p.
Motif Entropy Graph Kernel

Graph kernels have achieved excellent performance in graph classification tasks. In this paper, we propose a novel deep motif entropy graph kernel for the purpose of graph classification. For better capturing the differences between substructures, we gauge detailed information through a family of K-layer expansion motifs rooted at each node and combine the Weisfeiler-Lehman algorithm to subdivide motifs, which is further enhanced by motif entropy. Experiments on eight graph-structured datasets demonstrate that our method is able to outperform the state-of-the-art kernel methods for the tasks of graph classification.
Predicting millionaires from Ethereum transaction histories using node embeddings and artificial neural nets

We demonstrate the possibility of predicting the millionaire status of an externally-owned Ethereum account based on information queried out of the Blockchain and features describing its role in the network based on node embedding techniques. These features are fed into artificial neural nets and used for status prediction within a systematic model selection process. We report key methodological insights about training and comparing cross-validated deep and shallow neural networks that yield up to 82 percent testset accuracy in predicting the millionaire status of account users.
Modeling the effect of automated fiber placement induced gaps based on serial layer scanned point clouds

The gap is one of the most common defects during the AFP process, which has a nonnegligible impact on the mechanical properties of laminates. This paper proposes a novel three-dimension reconstructive gap model based on the in-process serial layer scanned point clouds, which can characterizes the gap features inside the laminate and pre-determine the mechanical performance before the subsequent procedures. The measured point cloud will be transformed into eligible and processible formats and then be used to generate a finite element grid using the element-based material property assignment method. The local material properties and fiber orientation are defined by previously assigned classes and grid geometries respectively. The effect of gap size on the in-plane tensile strength and damage evolution of laminates is investigated and both the experimental and simulation results find that the ultimate strength and stiffness show a significant decrease with the increase of gap size.
Optimizing Cascaded Control of Mechatronic Systems through Constrained Residual Reinforcement Learning

Cascaded control structures are prevalent in industrial systems with many disturbances to obtain stable control but are cumbersome and challenging to tune. In this work, we propose cascaded constrained residual reinforcement learning (RL), an intuitive method that allows to improve the performance of a cascaded control structure while maintaining safe operation at all times. We draw inspiration from the constrained residual RL framework, in which a constrained reinforcement learning agent learns corrective adaptations to a base controller’s output to increase optimality. We first revisit the interplay between the residual agent and the baseline controller and subsequently extend this to the cascaded case. We analyze the differences and challenges this structure brings and derive some principle insights from this into the stability and operation of the cascaded residual architecture. Next, we propose a novel actor structure to enable efficient learning under the cascaded setting. We show that the standard algorithm is suboptimal for application to cascaded control structures and validate our method on a high-fidelity simulator of a dual motor drivetrain, resulting in a performance improvement of 14.7% on average, with only a minor decrease in performance occurring during the training phase. We study the different principles constituting the method and examine and validate their contribution to the algorithm’s performance under the considered cascaded control structure.