Reviewerly/exHarmony huggingface dataset:
Lexicostatistics of the Relationship Between the Russian and Buryat Languages

The article presents the results of the statistical analysis of indicators of the matrix of word meanings, composed of consonant classes based on the frequencies of the Russian-Buryat dictionary using the Ratcliffe-Obershelp similarity measures. We calculated a number of complete matches of words over the entire field of the matrix by the method of known probabilities for detecting letters and classes in words, and by calculating the probabilities using Markov chains. The best result was obtained for calculating probabilities under the assumption of Markovian dependence of the classes. The influence of the rank of the matrix of measures of similarity of words on the degree of coincidence of lists has been studied. The average similarity measures change little as the rank of the matrix decreases to r = 64. Further reduction of the rank of the matrix is inexpedient. The quantitative characteristics of languages are determined - the probabilities of the appearance of consonant classes for dictionary words, the probabilities of words of different lengths in the Russian and Buryat languages. To test the null hypothesis (the observed similarities of the metrics are random), a permutation test was used, after which a conclusion was made about the non-random nature of the coincidence of words in the Russian and Buryat languages and the presence of borrowings in the languages.
Employee Performance: Organizational Culture, Occupational Health and Safety (K3), with Job Satisfaction as a Mediator

The study aimed to explore the impact of organizational culture and occupational health and safety on employee performance and job satisfaction. Using a quantitative approach with 170 purposively sampled respondents, the research employed smartPLS for analysis. Findings revealed a positive influence of organizational culture on performance and job satisfaction. However, occupational health and safety showed no direct impact on performance. Interestingly, organizational culture indirectly affected performance through job satisfaction as a mediating variable. Similarly, occupational health and safety influenced performance through job satisfaction. Overall, job satisfaction emerged as a key factor affecting employee performance.&#x0D; Highlights:&#x0D; &#x0D; &#x0D; Organizational Culture Impact: The study reveals a significant influence of organizational culture on employee performance, emphasizing the pivotal role that the workplace environment and values play in shaping individual effectiveness.&#x0D; &#x0D; &#x0D; Occupational Health and Safety Nexus: Interestingly, the research indicates no direct impact of occupational health and safety on employee performance. This finding prompts further exploration into the nuanced relationships between workplace safety measures and overall job effectiveness.&#x0D; &#x0D; &#x0D; Job Satisfaction as a Mediator: The study underscores the crucial role of job satisfaction as a mediator, demonstrating how it bridges the influence of organizational culture and health and safety measures to ultimately impact employee performance. This highlights the interconnected nature of workplace elements in shaping job satisfaction and, subsequently, performance.&#x0D; &#x0D; &#x0D; Keywords: Organizational Culture, Occupational Health and Safety (K3), Employee Performance, Job Satisfaction
Deep Learning

Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.
Fuzzy Inference and Fuzzy Logic Control

Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.
Machine Learning

Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.
Optimization: Soft Computing

Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.
Networking and Data Storage Mechanism in Blockchain

This paper proposes a blockchain networking scheme based on cloud platform deployment and its adapted data. Firstly, a subnet-based non-all-connection networking scheme is formed, which limits the scope of transaction confirmation to a limited number of nodes. Secondly, the data sharing mechanism of cloud computing divides the data into three levels: transaction data-sensitive state data-non-sensitive state data. The node only saves the transaction data related to the state transition to ensure the non-destructive modification, and the state data is supported by the cloud. The calculated features enable different levels of shared storage, maximizing storage space. The experimental results show that the scheme can provide new ideas for the storage and sharing of trusted data in the blockchain.
Word-Level Interpretation of Chatgpt Detector Based on Classification Contribution

The ChatGPT detector is considered a necessary task to standardize the use of ChatGPT. Difficulty interpreting the test process and results is a common problem with LLM. Most existing interpreters focus on attention visualization and rarely consider the classification process. This study presents a method to show the contribution of words to model predictions. Specifically, this study considers information from classification weight vectors, semantic vectors, and embedded input word vectors for a more complete interpretation of detector LLM. Three word-level attributes (word length, part of speech and word meaning) are compared with the conclusions of existing literatures to verify our method. Visual samples and analysis process can be found at https://github.com/salixc/WCC-DekunChen.
Inter-symbol Anti-interference Algorithm for 5G Communication System Based on Deep Learning

As a matter of fact, the fifth generation (5G) wireless technology requires huge capacity to ensure normal use of communications, and a large amount of data will cause interference between wireless communication systems. With this in mind, to reduce inter-symbol interference in wireless systems, MIMO-OFDM is employed, the rapid growth of deep learning has the potential to significantly enhance wireless system performance. In reality, applying deep learning to estimate channels in MIMO-OFDM systems can reduce channel errors as well as improve channel quality, thereby greatly reducing inter-code interference between systems. On this basis, this paper introduces various DL-based channel estimation and demonstrates its improvement in the efficiency of the system. According to the analysis, the usage of deep learning in 5G wireless communication systems has great advantages. In addition, the limitations and defects are also discussed at the same time. Overall, these results shed light on guiding further exploration of 5G communication.
Fraud detection based on FS-SMOTE model for credit card

In the financial security technology, credit card fraud detection technology is an important technical means, which collects and analyzes the credit card transaction data in a certain period of time, detects fraud in many credit card transactions, and takes the corresponding alarm response. At the same time, in view of the extremely imbalanced characteristics of credit card fraud customer data set, the number of minority samples is increased by Smote, which is a representative algorithm of oversampling technology. Logistic regression, KNN, Decision tree, Bagging and Stochastic gradient descent are used to construct fraud detection models. The credit card fraud data released on Kaggle platform were selected for verification. The experimental results show that the fusion model based on Feature selection and Smote greatly improves the accuracy and detection rate of credit card fraud detection as a whole.
Comparison and Evaluation of Classical Dimensionality Reduction Methods

As one of the tasks of unsupervised learning, data dimensionality reduction is faced with the problem of a lack of evaluation methods. Based on this, three classical dimensionality reduction methods such as PCA, t-SNE and UMAP were selected as the research object in this paper. This article selected 5 three-classification datasets and used the three methods mentioned above to perform dimensionality reduction. This paper plotted 3D scatter graphs after dimensionality reduction to analyze the differentiation effect of the data on different categories of the target variable. Then the data after dimensionality reduction was classified using random forest model and the classification accuracy was obtained. According to the 3D scatter plots and the accuracy of random forest, it is found that PCA has a good dimensionality reduction effect on most of the selected datasets, and t-SNE has a relatively stable dimensionality reduction effect. In contrast, UMAP has good dimensionality reduction performance in some individual datasets but lacks stability. Overall, this paper proposes a dimensionality reduction evaluation method that combines scatter-plot visualization results and classification models, which can effectively predict the performances of the dimensionality reduction methods for a variety of datasets, thereby promoting the comparison and selection of dimensionality reduction methods in the field of unsupervised learning.
Result Prediction of Wordle Word Guessing Game Based on SVM And BP Neural Network Multi-Input-Multi-Output Regression Prediction Model

Wordle is a popular daily puzzle game in which players can guess a five-letter word in six or fewer attempts. In order to predict the distribution column of user participation and guessing results, this paper established a support vector machine (SVM) regression model based on the historical data set of game results to predict the number of reports on March 1, 2023. Then, by extracting attribute features such as word frequency from words, the relationship between word attribute and report quantity under difficult mode is studied through multiple regression analysis and correlation coefficient analysis. Finally, a BP neural network multi-input-multi-output regression prediction model was established. The goodness of fit (R) of the model was 0.93671, and the distribution of correct guesses of the word "EERIE" was predicted. Through this regulation, the popularity of the game can be maintained to some extent, so that the number of people playing the game can maintain stability or growth.
The Establishment and Practical Application of UAV Fixed-Point Delivery Model Based on Difference Method

The problem of UAV fixed-point delivery is of great significance in the field of military and relief supplies delivery. In order to better calculate the distance of UAV delivery, this paper establish a UAV fixed-point delivery model. At the same time, this paper use the difference method to calculate, and the error analysis of the model, which verifies that the model has good applicability.
Research on Predicting Wordle Word Attempt Counts and Word Difficulty Classification Based on Machine Learning and K-Means Clustering Techniques

Wordle is a word-guessing mini-game that has gained tremendous popularity in recent years. As a result, there is a growing interest in analyzing Wordle's data to assist developers in problem-solving, predicting its popularity, and determining future directions. In this paper, we collected game data from Wordle users who shared their scores on Twitter between January 7, 2022, and December 31, 2022, using a Python program. We utilized machine learning and clustering techniques to develop models for predicting the number of word attempts and evaluating the game's difficulty grading. Subsequently, we conducted model testing using the word "EERIE" as an example to select the optimal model and verify its predictive accuracy. The research findings not only assist developers in enhancing user experience but also contribute to the broader field of game analytics, providing valuable insights for game design and player engagement. Ultimately, our study provides crucial data analysis support for the development of Wordle and reveals the potential and future directions of word-guessing games in the entertainment industry.
A Study on the Prediction Method Based on ARIMA-LSTM Combined Model in Wordle Game

In this paper, the paper propose a new integrated prediction model AMIRA-LSTM model, which integrates the advantages of each of the two models, firstly, the paper use ARIMA and LSTM models to fit for the known time series data of the wordle game respectively, and find that these two models fit better for the linear and nonlinear parts of the samples respectively, and in this paper, the paper substitute the ARIMA( p,d,q) model, which portrays the linear part of the data series, is substituted into the LSTM model to obtain the prediction about the residuals, thus modifying the prediction of the ARIMA model, and thus organically combining with the respective advantages of the LSTM model describing the nonlinear part of the data series, the final sample prediction results almost overlap with the actual results, and the RMSE and MAPE are 0.0256 and 0.33, respectively; this paper determines the word The RMSE and R2 are 0.2993 and 0.9021, respectively, with high model accuracy.
SVM Model Against Telecom Card Fraud Using GA Optimised Ten-Fold Cross-Testing

The increased number of payment methods also makes it easier for personal information to be stolen by criminals, and for criminals to take over financial payment accounts and steal money. With trillions of bank card transactions occurring every day, Credit Card Fraud Detection (CCFD) is a serious challenge, so this paper predicts "whether or not fraud occurs" by using six types of machine learning models. For problem 1, firstly, "mean, maximum, minimum, median, variance, standard deviation, quartile" are calculated for each indicator; secondly, data cleaning is carried out, and the data set is found to be free of missing values and outliers. Then the data preprocessing work was carried out, min_max normalisation and z-score standardisation were performed on the data. After that, correlation analysis was carried out, and the first four indicators were classified as negative indicators and the last three as positive indicators according to the characteristics of the indicators themselves. It can be found by calculating the Pearson correlation coefficient value after two data processing. Using the coefficient of variation method to calculate the weight of the seven "influence whether fraud" indicators. Finally, BP neural network model, decision tree model, random forest classification model, ELM model, SVM model, logistic regression model are established. For Problem 2, the four models constructed in Problem 1 are solved; to solve the BP neural network model: the data set is divided into training set and testing set according to the ratio of 6:4, and the sigmod function is used as the activation function. For BP neural network, "output &gt;0.5" is recorded as 1, i.e. fraudulent behaviour; "output &lt;0.5" is recorded as 0, i.e. non-fraudulent behaviour. Adjusting the learning rate and the number of iterations, the optimal average mean square error after optimal gradient descent is smaller. To solve the SVM model, the data set is divided into ten groups using the improved ten-fold cross-test, with one group as the training set and nine groups as the validation set, so as to obtain the model with the highest accuracy and the corresponding training data, and then the genetic algorithm is used to search for the optimisation of the kernel parameters in the SVM model on this basis. To solve the decision tree model, the training set and prediction set are divided into 7:3 and solved, and the number of leaf nodes is optimised. Solve the random forest classification model, divided into training set and prediction set according to 7:3 and solved, for similar accuracy choose the random forest classifier when the decision tree is less.
Functional Data Clustering Via Functional Mahalanobis Distance

As an exploratory data analysis method, functional data clustering aims to identify the underlying features of the observed data. In this context, this paper proposes a functional data clustering method based on functional Mahalanobis distance. As a distance-based non-parametric clustering model, the proposed method can effectively avoid the disadvantages of generative models and has excellent properties of decoupling and dimension standardization. Compared with other functional data clustering models, this method has lower computational complexity. In addition, the proposed method can be applied to any distance-based multivariate clustering method, thus generalizing it to the case of functional data. In practical data analysis, this paper compares the performance of this method with some other functional clustering methods, using k-means clustering as an example, and finds that it has better performance in terms of purity, adjusted Rand index, and computational speed. Finally, the idea of using Mahalanobis distance for functional data distance measurement can also be extended to construct kernel functions for measuring similarity between functional data samples, thus developing non-linear functional data analysis methods based on reproducing kernel theory.
Enhancing Arabic Vocabulary Acquisition: Evaluating Rosetta Stone Application for College Students

This study investigates the effectiveness of utilizing the Rosetta Stone application as a tool for learning Arabic vocabulary, particularly among college students at Rumah Binaan Indonesia Malang. Employing a case study approach involving observation, interviews, and documentation, the research demonstrates that the application facilitates independent learning, offering exercises with immediate feedback. The findings indicate that college students find the Rosetta Stone application user-friendly and flexible, allowing for convenient Arabic vocabulary acquisition at any time and place. This research sheds light on the potential of smartphone applications, such as Rosetta Stone, in supporting language learning endeavors for beginners.&#x0D; Highlight:&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study assesses the effectiveness of the Rosetta Stone application as a valuable tool for Arabic vocabulary acquisition, specifically tailored for college students at Rumah Binaan Indonesia Malang.&#x0D; Employing a comprehensive case study methodology, involving observation, interviews, and documentation, the research substantiates the application's capacity to facilitate independent learning with timely feedback.&#x0D; The findings underscore the user-friendly and adaptable nature of the Rosetta Stone application, enabling convenient Arabic vocabulary acquisition irrespective of time or location. This research illuminates the promising role of smartphone applications, like Rosetta Stone, in supporting language learning initiatives, particularly for beginners.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Keyword: Rosetta Stone Application, Arabic Vocabulary Learning, College Students, Language Learning Technology, Independent Learning