Reviewerly/exHarmony huggingface dataset:
The Connection between Education Input and Economic Growth―An Empirical Analysis Based on Eastern, Central and Western Cities

This paper applies the mathematical model research method, based on the Cobb-Douglas production function model, and selects 18 samples from the east, middle and west to study and analyze the relationship between education input and economic development. This study starts with the total investment in education and is divided into three aspects according to its source: financial education funds, private education funds and other non-financial education funds. At the same time, the employment population and physical capital are introduced to analyze the factors affecting economic growth more comprehends. By analyzing the statistical results, the following conclusions are finally obtained: on the one hand, the economy of each region will be positively promoted by the financial education funds, and the higher the economic level of a region is, the weaker the region is affected by the financial education funds; private education funds generally have no significant impact on the economy of a region; while other non-financial education funds have a positive effect on areas with high economic levels. On the other hand, for the western region where the economy is backward, there is a greater demand for both physical capital and fiscal education funds; while the eastern and western regions provide a large number of jobs due to developed industries and services. There is a certain demand for the labor force. At the end of the article, based on the above analysis, some suggestions were provided to the central and local governments.
Formation of a system of professional stereotypes in university students

The rapid change of paradigms of technology development in the XXI century requires flexible thinking from a specialist and a reduction in the degree of stereotypical professional thinking. However, any professional activity is based on a system of stereotypes. This article studies the formation of a system of professional stereotypes among university students in the learning process, and also identifies positive and negative manifestations of the developed system of stereotypes. The methodological basis of the research was the analysis of scientific publications and generalized practical experience. The place of stereotypes in the educational system of the university, the positive and negative qualities of stereotypes, the ways of formation and complexity of changing the system of professional stereotypes, as well as the need for a creative approach to professional activity and periodic changes in stereotypes are considered. It is shown that, on the one hand, university education should contribute to the formation of a system of necessary professional stereotypes among students and the ability to use its advantages competently, on the other - to develop a willingness to adjust this system when the situation changes and change stereotypes in cases of need, to teach to avoid stagnation and to be in constant development, following the principles of continuing education.
Image Attribute Migration Based on Decoupling and Adaptive Layer Instance Normalization

The issue of image attribute migration is one of the hot research topics in the field of computer vision, which has received extensive research interest. However, current unsupervised image attribute migration models using symmetric generative adversarial network structure do not work well on datasets with large geometric variations, where the results lack diversity and are of low quality. To address these problems, we present an image attribute migration model based on decoupling and adaptive layer instance normalization. First, a codec structure based on a decoupled representation is constructed as the generator, and an adaptive layer instance normalization operation is used in the decoder. Then, the iterations of the model are constrained by various improved loss functions. We conducted controlled experiments and compared the results of our method with other methods using several datasets with large geometric variations. The experimental results demonstrate that the proposed method can achieve high quality and diverse image attribute migration.
SYSTEM OF PSYCHOLOGICAL AND DIDACTIC TASKS PLAYERS IN THE EDUCATIONAL PROCESS FACE IN THE PARADIGM “TEACHERS – SCHOOLCHILDREN – PARENTS” AMID THE COVID-19 PANDEMIC

The paper is aimed at substantiating the system of psychological and didactic tasks all the players in the educational process of general secondary schools (teachers, schoolchildren, parents) face in the paradigm “teachers – schoolchildren – parents” amid the Covid-19 pandemic. The current research is carried out within the framework of the project “Organization of Educational Process in the Content of Unpredicted Changes (the Covid-19 Pandemic): Comparative Analysis (Ukraine – EU countries)” (Registration No 0121U108690). The solutions of the determined within the research psychological and didactic tasks are targeted at inventing psychodidactic support for parents (possibly grandparents and other relatives) to be successfully used to prevent misunderstandings and even possible conflicts provoked by close communication and coexistence of people who belong to different generations (silent generation, baby boomers, generation X, millennials (generation Y) and generation Z) within the lockdown and the postpandemic period.
Challenges Faced in the Enterprise Resource Planning Material Management Section when Transitioning Towards Agile Software Development

Agile Software Development Approach is the buzzword for the Organizations working with Traditional Software Development Approach. Scrum is one of the most vital frameworks used when working towards Agile Software Development projects. Material Management is one of the salient processes of the Supply Chain Management in transforming inputs into outputs. Extreme Programming is more technical in nature and Scrum is a People focused Management approach. This scrum framework is customizable according to the project teams and it is adopted widely to effectively manage software projects. Scrum framework fits into the groove for delivering quality product at a faster pace with minimal continuous feature delivery at frequent intervals. The minimal Marketable feature is delivered at the end of each Sprint. A Sprint is an iterative activity which ends up at regular intervals. But, the way of customizing the process and giving a minimal feature delivery at the end of each Sprint in the projects act as a major challenge. Other challenges resulted in the team collaboration and the involvement of the team members in the project and in integrating the minimal marketable feature. Syncing with the product owner and the other stake holders, and participation in the sprint planning were some of the issues that the team faced. The success of the entire team in implementing the Agile Software Development approach in the Enterprise Resource Planning Material Management Section is discussed.
Curating China’s Cultural Revolution (1966–1976): CR/10 as a Warburgian Memory Atlas and Digital Humanities Interface

CR/10 is a digital oral history platform that aims to collect and preserve cultural memories of China’s Cultural Revolution (1966–76). With a rhetorical analysis of the design features and curation processes of the CR/10 website, this article discusses the functions of CR/10 as a Warburgian memory atlas that shape the nonlinear, multifaceted narratives of a historical incident. Alongside this rhetorical analysis, I also conducted three sets of user experience studies with over thirty participants both within and outside the academy, including an ethnographic conference observation, a virtual ethnography of an online book group, and several semi-structured interviews, to examine CR/10’s usability and propose new design opportunities to empower the interface. This article offers a strong case for the datafication of cultural memories and contributes to digital archives and humanities interface design with an innovative theoretical lens.
Ongoing dynamic calibration produces unstable number estimates.

Whether estimating the size of a crowd or rating a restaurant on a five-star scale, humans frequently navigate between subjective sensory experiences and shared formal systems. Here we ask how people manage this in the case of estimating number. We present participants with arrays of dots and ask them to report how many dots there are. Our results produce two novel findings. First, people's estimates are best fit by a bilinear function in log space, rather than the traditional power law described in previous literature. Second, we find that people's estimates do not have a stable coefficient of variation at higher magnitudes, and that the likely cause of this is a "drift" in people's estimate calibration over many trials which has not previously been identified. Building on these results, we present a model of the mapping function from subjective numerosity to symbolic number that relies primarily on a constrained set of previous estimates and familiar numerosities, rather than the robust internal scale used in existing models. Our model is able to generate an accurate mapping with limited data and reproduce notable aspects of estimation seen in our experimental results. This suggests that human number estimation, and perhaps other domains in which we must navigate between subjective representations and formal systems, is governed by a relatively simple decision process that primarily seeks to maintain consistency with previous estimates. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Sensitivity Analysis of Genetic Algorithms for Optimization of Operating Conditions for Protein Production Plants

This work introduces genetic algorithms, which will be investigated and used to perform sensitivity analysis. Genetic algorithms are part of a collection of stochastic optimization algorithms based on the concepts of biological evolutionary theory. This research deals with the problem of the search for optimal investment cost of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem by using Genetics Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four point crossover operator, which take into account, the uncertainty on the demand using Gaussian process modeling. Experiments indicated that relatively good results could be obtained using 4-point crossover with an applied rate of 0.7 and mutation rate ranged between 0.01 and 0.09 promised to give best performance. The results (number and size of equipment, investment cost, production time (Hi), CPU time and Idle times in plant) obtained by GAs are much more rapidly than mixed integer linear programming. This methodology can help the decision makers and constitutes a very promising framework for finding a set of “good solutions”.
Observations on Annotations

The annotation of textual information is a fundamental activity in Linguistics and Computational Linguistics.This article presents various observations on annotations.It approaches the topic from several angles, including Hypertext, Computational Linguistics and Language Technology, Artificial Intelligence and Open Science.Annotations can be examined along different dimensions.In terms of complexity, they can range from trivial to highly sophisticated, in terms of maturity from experimental to standardised.Annotations can be annotated themselves using more abstract annotations.Primary research data such as, e.g., text documents can be annotated on different layers concurrently, which are independent but can be exploited using multi-layer querying.Standards guarantee the interoperability and reusability of data sets.The chapter concludes with four final observations, formulated as research questions or rather provocative remarks on the current state of annotation research.
Remote Real-Time Monitoring System for Mine Slope Based on Cloud Computing

A remote real-time monitoring system was developed for the remote real-time monitoring of mine slope deformation and internal forces. The system is based on cloud-computing technology and a 5S multimedia streaming monitoring data transmission system. It has now been applied to an open-pit iron mine in Nanfen to monitor the horizontal displacement of potential sliding surfaces. Compared with geometric monitoring methods in traditional geodetic surveying, the results show that this monitoring system has higher accuracy (0.01 mm) and the ability to monitor in real time for 24 h. These features can realize early warnings of mine slopes from multiple dimensions, and provide a guarantee for safe, continuous and the efficient production of mines. In addition, it can provide a certain reference for the prevention and control of mine slope disasters, and is beneficial to the application of information-based early warning technology.
A New Robotic Learning Activity Design to Increase the Figural Creativity: Originality, Elaboration, Flexibility, and Fluency

Preparing a generation that possesses the skill of Figural Creativity (FC) skills is important in education because figural creativity skills are needed in the industrial revolution 4.0 (IR 4.0) era.Figural creativity skill is the ability to create something new, a new manner, and produce something different from its initial state in providing solutions to the problems faced.One of the IR 4.0 technologies that can improve creativity is robotic technology, but the implementation of robotic technology in education still requires an appropriate learning activity.This study aims to design the new robotic learning activities and analyze their implementation to increase Figural creativity variables.Figural creativity variables consist of four variables: elaboration, flexibility, fluency, and originality.A total of 23 students were recruited in a user study experiment, ages 9-12 years old.Figural creativity skill of students measured by Figural Creativity Test (TKF).This study used mix method approach.The results showed that the new robotic learning activity design could fulfill the valid criteria; expert suggestions are subject to fixing this learning activity.The new robotic learning activity design was implemented in the robotic course.The paired sample t-test showed that robotic learning activity design has a significant difference between pre-test score and post-test score, and it can improve students' elaboration, flexibility, fluency, and originality.It has a growing impact on all figural creativity variables (fluency = 60.9%,flexibility=56.6%,elaboration = 26.6%,originality =39.3%).
An Efficient Image Captioning Method Based on Generative Adversarial Networks

Image captioning is usually based on the mainstream encoder-decoder framework, the accuracy of the encoders and the generation capability of the decoders directly affect the quality of image captions. But no matter how the encoders and the decoders are combined, the generated captions will only imitate the ground-truth captions, and its diversity is suppressed. To generate more accurate and diverse captions while exploring the impact of different encoder-decoder frameworks for the quality of the generated captions, we conducted an exploration of an efficient image captioning method based on GAN. Specifically, we use ResNet152, EfficientNet-B0, and EfficientNet-B2 as the encoders to extract image features, GRU or Transformer as the decoders to generate captions, respectively. Based on MS COCO2014 dataset, a large number of experiment results show that the EfficientNet-B2+GRU framework has best performance, training time is reduced, BLUE-4 score increases by 0.6, parameters reduce by 0.66 million, which can be used as an efficient encoder-decoder framework for generating diverse captions.
Analysis of the Driving Role of Computer Science and Technology on the Development of the Internet of Things

In recent years, computer science and technology applications in a variety of different fields have also attracted the attention of many experts and scholars. In the real society, some experts and scholars have linked the progress of computer science and technology and the Internet development together, and from here, we can also further explore the new application ideas of the Internet of Things technology in computer science and technology. This article also presents the difficulties of the development and progress of the significance and characteristics of computer science and technology. At the same time, the paper also points out the application principles, development ways and development strategies of computer science and technology in the development of the Internet of Things. The rich development of computer science and technology has also laid a certain foundation for the quality of the development of the Internet of Things.
Data Mining Algorithm of Hotel Customer Data Based on Mobile Computing

Mobile computing has gradually emerged and developed vigorously along with the development of computer technology, Internet communications and networks. As a new and practical and efficient technical means, mobile computing has outstanding advantages such as strong operability, wide range of use, high information processing capabilities and fast calculation speed, and has been widely used in all aspects of social life. At the same time, the hotel industry, as an important part of the service industry, needs to analyze user needs and provide personalized customized services, so there is a greater demand for hotel customer data mining. In the mobile computing environment, the research of customer information data mining has become one of the indispensable and important contents of today's social development. This article adopts experimental analysis and data analysis methods to understand the effect of clustering analysis of different data mining algorithms through experimental research, so as to improve the ability of the algorithm to analyze customer information. According to the experimental results, among these algorithms, the clustering time of the FDBSCAN algorithm is the shortest, which can achieve the clustering effect of the DBSCAN algorithm, and the clustering time and time complexity are both smaller than the DBSCAN algorithm, and it takes advantage of reducing the time complexity.
Comparison of Decision Tree Classification Methods and Gradient Boosted Trees

The purpose of this research is to analyze the C4.5 and Random Forest algorithms for classification. The two methods were compared to see which one in the classification process was more accurate. The case is the success of university students at one of the private universities. Data is obtained from the https://osf.io/jk2ac data set. The attributes used were gender, student, average evaluation (NEM), reading session, school origin, and presence as input and success as a result (label). The process of analysis uses Rapid Miner software with the same test parameters (k-folds = 2, 3, 4, 5) with the same type of sample (stratified sample, linear sample, shuffled sampling). The first result shows that the sample type test k-fold (stratified sampling) achieved an average accuracy of 55.76 percent (C4,5) and 5618 percent (Random Forest). The second result showed that the kfold (linear sampling) sample test achieved an average precision of 58.06 percent (C4.5) and 6506 percent. (Random Forest).The third result shows that the k-fold test with the sampling type has averaged 58.68 per cent (C4,5) and 60,76 per cent (shuffled sampling) precision (Random Forest). From the three test results, in the case of student success at a private university, the Random Forest method is better than C4.5.
Improving the classification of high dimensional class-imbalanced data using the Chaos particle swarm optimization with Levy Flight

Nowadays classification of imbalanced datasets has gained a lot of attention in the field of data mining and machine learning. This type of classification is a challenging task due to imbalanced distribution of the data. In addition, conventional algorithms do not perform well in classification of such high dimensional data. Researchers have proposed many techniques for improving the classification of high dimensional class-imbalanced data. These approaches fall into five categories: 1- Cost-sensitive learning, 2- Algorithm level, 3- Data level, 4- Ensemble learning and 5- Feature selection methods. In this paper, we have exploited cost-sensitive and feature selection methods. Our proposed method first uses fuzzy c-means clustering (FCM) to estimate the probability of each sample being a member of the minority class. Using this method, samples which are hard to classify are determined based on their probabilities and then the cost-sensitive learning is employed to estimate their cost. Finally, particle swarm optimization algorithm enhanced by levy flight is applied for feature selection and a SVM model is trained for classification of the data. By using particle swarm optimization alongside with SVM classifier, best features are determined and misclassification rate of minority samples is reduced. The evaluation of the proposed method is done by using G-Mean metric and 10-fold cross validation. The results of conducted experiments show the superiority of the proposed method compared to other approaches in classification of high dimensional class-imbalanced data.
Numerical Discrimination in &lt;i&gt;Drosophila melanogaster&lt;/i&gt;

Sensitivity to numbers is a crucial and evolutionarily conserved cognitive ability. The lack of experimental models amenable to systematic genetic and neural manipulation has precluded discovering circuits required for numerical cognition. Here, we demonstrate that in a two-choice task Drosophila fruit flies spontaneously prefer sets containing more objects. This preference is determined by the ratio between the two numerical quantities tested, a characteristic signature of numerical cognition across species. Individual flies maintained their numerical choice over consecutive days. Using a numerical visual conditioning paradigm, we found that flies are capable of associating sucrose with numerical quantities and can be trained to reverse their spontaneous preference for large quantities. Finally, we show that silencing LC11 neurons reduces the preference for more objects, thus identifying a neuronal substrate for numerical cognition in invertebrates. This discovery paves the way for the systematic analysis of the behavioral and neural mechanisms underlying sensitivity to numerosity.
Embodying nature: Discovering eco-consciousness through sensate experience

Eco-consciousness has its foundation in an active relationship between the individual soma and the larger body of the natural world. Embedded within this relationship is our innate sensorimotor intelligence and our natural instinct as children to spontaneously play as living, sensing, expressive bodies coming to know ourselves and our environment. In this practice reflection, I delineate a map and a process with sample activities for embodied creative engagement to cultivate our capacity to witness, contact, mirror, respond and rest in partnership with the living planet. This approach – ‘embodying nature’ – is highly adaptable for various ages and degrees of experience, inviting participation at any level and in whatever natural environment is available.
Does population growth have any impact on economic growth?: Evidence from Tanzania.

AI Publications is an open access peer review international journal to publish the article in the field of Agriculture, Horticulture, Food science, Rural Development, Environment and Health Research, Chemistry, Mathematics,Physics.