Reviewerly/exHarmony huggingface dataset:
Predication and Photon Statistics of a Three-Level System in the Photon Added Negative Binomial Distribution

Statistical and artificial neural network models are applied to forecast the quantum scheme of a three-level atomic system (3LAS) and field, initially following a photon added negative binomial distribution (PANBD). The Mandel parameter is used to detect the photon statistics of a radiation field. Explicit forms of the PANBD are given. The prediction of the Mandel parameter, atomic probability of the 3LAS in the upper state, and von Neumann entropy are obtained using time series and artificial neural network methods. The influence of probability success photons and the number of added photons to the NBD are examined. The total density matrix is used to compute and analyze the time evolution of the initial photonic negative binomial probability distribution that governs the 3LAS–field photon entanglement behavior. It is shown that the statistical quantities are strongly affected by probability success photons and the number of added photons to the NBD. Also, the prediction of quantum entropy is achieved by the time series and neural network.
Fuzzy Synergetic Control for Dynamic Car-Like Mobile Robot

Abstract This paper aims to present the dynamic control of a Car-like Mobile Robot (CLMR) using Synergetic Control (SC). The SC control is used to make the linear velocity and steering velocity converge to references. Lyapunov synthesis is adopted to assure controlled system stability. To find the optimised parameters of the SC, the grey wolf optimiser (GWO) algorithm is used. These parameters depend on the best-selected fitness function. Four fitness functions are selected for this purpose, which is based on the integral of the error square (ISE), the integral of the square of the time-weighted error (ITSE), the integral of the error absolute (IAE) and the integral of the absolute of the time-weighted error (TIAE) criterion. To go further in the investigation, fuzzy logic type 2 is used to get at each iteration the appropriate controller parameters that give the best performances and robustness. Simulations results are conducted to show the feasibility and efficiency of the proposed control methods.
Professional development of teachers in the context of the lifelong learning model: The role of modern technologies

The relevance of this research is due to the need to study the possibilities of lifelong learning in modern educational settings. Its central aim was to consider the influence of modern technologies on the level of professionalism of teachers in the process of lifelong learning. For this, a specially developed model aimed at increasing student motivation was introduced, and corresponding pre- and post-surveys were conducted. The results of the experiment showed an increase in the demand for lifelong learning in the modern learning environment and the active use of modern technologies. In the meantime, the outcomes of the repeated survey demonstrated the expansion of the list of motivation factors. Prospects for further research are seen in the possibility of applying the results obtained when analyzing the features of lifelong learning in other countries in the context of the modern education system in a comparative aspect. Apart from this, future studies use the achieved findings to scrutinize the influence of pedagogical innovations on lifelong learning effectiveness.&#x0D; &#x0D; Keywords: Education system, lifelong learning, modern technologies, pedagogical innovation,; professionalism.
Thoughts on Issues Related to Several Opinions of the General Office of the State Council on Deepening Integration of Industry into Education

The Opinions of the General Office of the State Council on Deepening the Integration of Industry and Education (hereinafter referred to as the Opinions) reaffirms some of the educational systems and principles that have evolved in recent years in the field of higher education and vocational education in China, and is also of great significance in cracking the dilemma of vocational education development. The introduction of the Opinions has brought vocational education in China into a new phase, and the policies for the integration of industry and education are gradually being optimised. The current policy of integration of industry and education has a successful practice, on the other hand, also the development of integration of industry and education still faces many bottlenecks and constraints, this paper will take this as a starting point for analysis and reflection.
Synthetic feature pairs dataset and siamese convolutional model for image matching

In a previous publication [1], we created a dataset of feature patches for detection model training. In this paper, we use the same patches to create a new large synthetic dataset of feature pairs, similar and different, in order to perform, thanks to a siamese convolutional model, the description and matching of the detected features. We thus complete the entire matching pipeline. The accurate manual labeling of image features being very difficult because of their large number and the various associated parameters of position, scale and rotation, recent deep learning models use the result of handcrafted methods for training. Compared to existing datasets, ours avoids model training with false detections of the extraction of feature patches by other algorithms, or with inaccuracy errors of manual labeling. The other advantage of synthetic patches is that we can control their content (corners, edges, etc.), as well as their geometric and photometric parameters, and therefore we control the invariance of the model. The proposed datasets thus allow a new approach to train the different matching modules without using traditional methods. To our knowledge, these are the first feature datasets based on generated synthetic patches for image matching.
Perceptions about the applicability of robot technology in the tourism industry

In the research, it is aimed to determine the applicability of robot technology and the importance of technological innovations in the tourism industry. The populatin of the research consists of academicians, managers and students in the tourism industry. In the research, the "convenience sampling" method was used, in which everyone who participated in the research could be included in the sample. The research data was collected from tourism students, academicians and managers who participated in the Mersin University Faculty of Tourism Career Days and the IVth International Eastern Mediterranean Tourism Symposium held in 2019. The Cronbach Alpha test was applied for the reliability of the scale, along with the frequency distributions, percentiles, mean values, standard deviations and correlation coefficients from the descriptive statistics of the obtained data. In the research findings, it is accepted that airports, housekeeping activities, tour operator and travel agency services and hotel receptions are the most applicable areas of robot technology in the tourism industry.
Etiquette Behavior in the Context of University Organizational Culture

The aim of this study is to reveal interrelationship between the notions of etiquette behavior of students and teachers and the characteristics of organizational culture at universities. Methods. The research is based on a survey. The sample included 141 students and 69 teachers from Russian universities. A set of diagnostic methods was used to gather the empirical data. Data on teachers’ and students’ attitude to institutionalization of etiquette rules and dress code as well as students’ compliance with etiquette rules were collected using the author’s questionnaire; data on evaluation of etiquette functions were obtained through the questionnaire Subjective Evaluation of Etiquette Functions’ Significance by O.I. Danilenko, data on existing and preferred organizational culture in the respondents’ universities – through Organizational Culture Assessment Instrument (OCAI) by K. Cameron and R. Quinn. Results. Correlations between indicators, characterizing etiquette behavior and current organizational culture type, were identified in both groups: positive for clan and adhocracy culture type, negative – for hierarchical. Statistically significant positive correlations between evaluation of etiquette functions' significance and evaluation of students’ compliance with etiquette rules and clan culture, negative correlations between the same indicators of etiquette behavior and hierarchical culture were revealed in the students’ group. In teachers’ group, evaluation of etiquette functions' significance, positive attitude to institutionalization of etiquette rules and dress code are correlated positively with clan and adhocracy organizational culture, negatively – with hierarchical. Conclusion. The gathered data indicate: the higher both students and teachers evaluate characteristics of clan and adhocracy culture, the higher is their evaluation of the importance of etiquette behavior. The higher are values of hierarchical culture characteristics, the lower is evaluation of etiquette importance.
Image defogging system and algorithm research based on MATLAB platform

Abstract Since the Reform and Opening up, the characteristics of China’s high-quality economic development have become more distinctive, the adjustment of economic and social structure has been continuously optimized, the kinetic energy has been continuously enhanced, and the degree of industrialization has been significantly improved. However, it has also caused some environmental problems, and haze pollution is one of them. The haze weather with low visibility has a certain impact on people’s daily travel, transportation and navigation. Because the images taken under the haze condition will be degraded to varying degrees and the image details will be lost, the effect of identifying and processing the degraded images in the later stage will be reduced. Therefore, in order to clear and restore the degraded image, we developed a defogging system based on MATLAB software. The system mainly uses histogram equalization, MSR and dark channel a priori defogging algorithms to restore fog degraded images. The results indicate that various defogging algorithms are able to enhance the clarity of the picture to varying degrees. The successful implementation of the defogging system lays a theoretical foundation for the in-depth research and application of fog image defogging processing technology, and has good technical operability.
Writing assistant scoring system for English second language learners based on machine learning

Abstract To reduce the workload of paper evaluation and improve the fairness and accuracy of the evaluation process, a writing assistant scoring system for English as a Foreign Language (EFL) learners is designed based on the principle of machine learning. According to the characteristics of the data processing process and the advantages and disadvantages of the Browser/Server (B/S) structure, the equipment structure design of the project online evaluation teaching auxiliary system is further optimized. The panda method is used to read the data, the clean method is used to realize the data preprocessing, the model test is carried out, the cross validation method is selected, the data is divided in advance, and the process of programming the problem scoring system is further optimized, the automatic scoring technology is constructed by English teaching recognition module, feature extraction module and scoring module, the table structure of programming problems is designed, the auxiliary evaluation program of English writing is designed, and the design of writing auxiliary scoring system is completed. The analysis of the experimental results shows that the accuracy of the system is close to 90%, and the total average difference is 0.56. The system can normally take out a variety of test papers. Considering the subjectivity of manual scoring and the impact of key code setting on scoring, the carefully set key code can effectively improve the scoring accuracy of the system. The scoring strategy of the automatic scoring system is effective and the scoring effect is good, and it can be used in practical application.
Analisis Sentimen Mengenai Vaksin Sinovac di Media Sosial Twitter Menggunakan Metode NaÃ¯ve bayes Classification

The Sinovac vaccine is an example of a type of inactivated vaccine. The government bought Sinovac, Novavax, AstraZeneca, and Pfizer vaccines. This vaccine is used to treat the Covid-19 pandemic. This vaccine is used to treat the Covid-19 pandemic. The role of the Indonesian people in expressing and stating the pros and cons often involves public services that are easily accessible by many people, namely social media, one of which is Twitter. This can be used as material to analyze who produces data in support of decisions. The technique that can be used is sentiment analysis. The method used in this study is the NaÃ¯ve bayes Classification. The purpose of this study was to determine the value of sentiment analysis on the Sinovac vaccine using the Naive Bayes Classification method on Twitter social media using Indonesian. The result of this research is the final probability value based on the condition 0.000002765 for positive and 0.000000359 for negative. A response with a positive comment has a greater probability of a response with a negative comment.
A Study of Total Variation Regularization in Digraph Signal Denoising

In this paper, a study of total variation (TV) regularization in digraph signal denoising is presented. First, four definitions of the TV of digraph signals are described to measure the variation or smoothness of digraph signals. Then, an optimization method is presented to solve the digraph signal denoising problem by minimizing the signal distortion with TV regularization. The optimal solution can be obtained by solving the matrix inversion. Two iterative methods are also derived to implement the denoising operator by using Neumann series method and steepest descent method such that matrix inversion can be avoided. Finally, the sensor network data is used to make experiments to study which definition of TV provides the highest signal to noise ratio (SNR) in digraph signal denoising application.
A NEED ANALYSIS OF LEARNING ENGLISH FOR COMPUTER SCIENCE STUDENTS

English learning materials for Computer Science Department students which are suitable with learners’ needs are scarcely available. Thus, a study on analysis of students’ need is necessary. The purpose of this study was to find out students’ need on English based on ESP approach of Computer Science Study Program at USN Kolaka. This study used need analysis with survey design. The instruments used in this study were a semi-close ended questionnaire and interview. The questionnaire was distributed to 50 respondents and four students were interviewed. All of them were from Computer Science students at Universitas Sembilanbelas November Kolaka. It was found out that the students’ goal to learn English is to compete in the world of work. The expected level of English is advanced level. The priority skills are speaking and reading skills focusing on vocabularies related to computer science. In speaking skill activity, most students prefer to make a dialogue and then practice with friends. Meanwhile, in reading skills, students prefer activities that train their understanding and concentration like answering questions from a reading text.Keywords: computer science, ESP, learning needs, need analysis, target needs
Effects of Music-Based Intervention on English Proficiency among Primary School Pupils in Selangor

Based on existing research, the current status of English literacy skills among low performing pupils in national primary schools is a serious cause for concern.Early intervention to tackle this problem is critical to help children catch up with the expected educational standards which include speaking, listening, reading, and writing skills at each level of study.Therefore, this study set out to investigate the effects of music-based intervention on English proficiency among primary school pupils by using improvised teaching materials from the CEFR Year 3 syllabus as material which ought to be able to satisfy students' require and, both present future needs and to examine the participation level of pupils from the music-based intervention.This research employed a quantitative approach with a pre-test-post-test quasi-experimental design.A total of forty Year Three Students (N = 40 pupils, divided into 2 groups; quasi-experimental and control group) participated in the study.Data was collected using speaking assessments and observation checklist.The findings of the study showed that there was a significant difference between the quasi-experimental groups following music-based intervention with improvised materials.Students who had gone the through music-based intervention using improvised materials had showed improvement not only in the content and vocabulary in the materials, but also their speaking skills, especially their pronunciation and fluency.Results also illustrated that the inclusion of a music-based intervention and improvised materials in English class appears to have motivated the pupils to participate actively in the class discussion.
Semantic Web for Meteorological and Oceanographic Data

Data science research is now transforming the world of data and information technology into a new crucial paradigm. Many academic researchers have gotten interested in this matter. The goal of this study is to provide a semantic web technique for the oil and gas industry that includes an architecture for data integration. Semantic web technologies allow the process of extending the web in which information can be exchanged and shared in a meaningful way. However, with the amount of data increasing every day, there is a need for a semantic web system in all data industries. The modelling of ontologies and relational databases (RDB) into a resource description framework (RDF) is an important part of constructing the semantic web, which this study has adopted. Oil and gas data, in particular, meteorological and oceanographic (MetOcean) data, was used in this study. Applications such as Database to RDF Query (D2RQ), prot&#x00E9;g&#x00E9;, and web ontology language (OWL) reasoners were utilised for setup and performance in putting the findings into practice. The Berlin SPARQL Benchmark (BSBM) was used to analyze the performance of MetOceanSemWeb in order to provide a complete assessment of the findings. MetOceanSemWeb has therefore proven to be sufficient for adoption in the MetOcean sector. Furthermore, a scalability on D2RQ system was discovered due to the performance comparison.
COVID 19 Detection Application at Siti Fatimah Hospital Method of Using Deep Learning

WHO (Whorld Health Organization) declared Covid-19 a pandemic.The SARS-CoV-2 virus has infected 5,817,317 people with 216 countries infected as of May 28, 2020.For this reason, a solution is needed to break this covid chain, one of which is a detection application that can be used as a supporter in decision making to find out people who are infected.indicated this covid-19.This research was conducted at Siti Fatimah Hospital, South Sumatra Province using the method Machine learning by using yahoo Convolutional Neural Network (CNN) which is used as image process.
Thaler strikes again, down under: artificial intelligence systems as inventors not ruled out (yet)

Abstract Thaler v Commissioner of Patents [2021] FCA 879, Federal Court of Australia, 30 July 2021 The Federal Court of Australia finds a patent application was correctly lodged despite naming an artificial intelligence system as its inventor.
Clustering by the Probability Distributions From Extreme Value Theory

Clustering is an essential task to unsupervised learning. It tries to automatically separate instances into “coherent” subsets. As one of the most well-known clustering algorithms, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means assigns sample points at the boundary to a unique cluster, while it does not utilize the information of sample distribution or density. Comparably, it would potentially be more beneficial to consider the probability of each sample in a possible cluster. To this end, this article generalizes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means to model the distribution of clusters. Our novel clustering algorithm, thus, models the distributions of distances to centroids over a threshold by the generalized Pareto distribution (GPD) in extreme value theory. Notably, we propose the concept of centroid margin distance, use the GPD to establish a probability model for each cluster, and perform a clustering algorithm based on the covering probability function derived from the GPD. Such a GPD <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means, thus, enables the clustering algorithm from the probabilistic perspective. Correspondingly, we also introduce a naive baseline, dubbed as generalized extreme value (GEV) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means. The GEV fits the distribution of the block maxima. In contrast, the GPD fits the distribution of distance to the centroid exceeding a sufficiently large threshold, leading to a more stable performance of GPD <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means. Notably, GEV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means can also estimate the cluster structure and, thus, perform reasonably well over classical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means. Thus, extensive experiments on synthetic and real datasets demonstrate that GPD <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means outperforms competitors.
Fault Prediction with Static Software Metrics in Evolving Software: A Case Study in Apache Ant

Software testing is an integral part of software development.Not only that testing exists in each software iteration cycle, but it also consumes a considerable amount of resources.While resources such as machinery and manpower are often restricted, it is crucial to decide where and how much effort to put into testing.One way to address this problem is to identify which components of the subject under the test are more error-prone and thus demand more testing efforts.Recent development in machine learning techniques shows promising potential to predict faults in different components of a software system.This work conducts an empirical study to explore the feasibility of using static software metrics to predict software faults.We apply four machine learning techniques to construct fault prediction models from the PROMISE data set and evaluate the effectiveness of using static software metrics to build fault prediction models in four continuous versions of Apache Ant.The empirical results show that the combined software metrics generate the least misclassification errors.The fault prediction results vary significantly among different machine learning techniques and data set.Overall, fault prediction models built with the support vector machine (SVM) have the lowest misclassification errors.