Reviewerly/exHarmony huggingface dataset:
Arm Movement Analysis Technology of Wushu Competition Image Based on Deep Learning

In order to improve the recognition accuracy of action poses for athletes in martial arts competitions, it is considered that a single frame pose does not have the temporal features required for sequential actions. Based on deep learning, this paper proposes an image arm movement analysis technology in martial arts competitions. The motion features of the arm are extracted from the bone sequence. Taking human bone motion information as temporal dynamic information, combined with RGB spatial features and depth map, the spatiotemporal features of arm motion data are formed. In this paper, we set up a slow frame rate channel and a fast frame rate channel to detect sequential motion of images. The deep learning model takes 16 frames from each video as samples. The softmax classifier is used to get the classification result of which action category the human action in the video belongs to. The test results show that the accuracy and recall rate of the arm motion analysis technology based on deep learning in martial arts competitions are 95.477% and 92.948%, respectively, with good motion analysis performance.
Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

In order to explore the current income gap, a method based on the PSO algorithm in the edge computing environment is proposed. PSO calculates and simulates bird flock foraging activities, the Frank Heppner biological group model, and the three rules in bird activities. After studying the activities of these natural creatures, abstract problems are quantified and similar models established. The Gini coefficient is calculated by using grouped data, and the grouping basis is also innovative. The quantile grouping method is adopted, which can effectively solve the difference between the concentration index and the Gini coefficient, and the Gini coefficients of each year can be added up to finally get the Gini coefficient of the stock income. Experimental results show that the Gini coefficient of traffic income in 2017 and 2018 had dropped significantly, but the variation of the Gini coefficient of stock income (Delta CG) was still greater than 0. Obviously, the adjustment speed of the Gini coefficient of stock income was lagging behind, as was the Gini coefficient of traffic income. We found that after 1986, the facilitation effect was greater than the dilution effect, and the facilitation effect continued to push up the stock income gap, which indicated more income flow to the high-income group, with the income flow gap showing an upward trend and the upward trend becoming more and more obvious. It has been proved that the PSO algorithm can effectively identify the income gap in the edge computing environment, and the corresponding policy suggestions are given.
Intelligent Agents

In the previous chapter, we subscribed to the rational agent definition of AI. In this chapter, we define rational agents in an environment. We describe how AI works from the lens of input-output mappings, feedback loops, and rewards. Toward the end of the chapter, we discuss the probabilistic nature of AI models.
Comparative Study and Implications of Education Informatization Policies in China and the United States

Education informatization is an important symbol of education modernization construction.With the rapid development of information technology, information technology has a profound impact on education, and countries fully recognize the strategic importance of artificial intelligence for their own countries, and the United States is the first country to introduce information technology into the field of education.In order to promote social equity, the U.S. has been exploring in the field of using education informatization to promote educational equity in different regions, and has achieved certain results.Therefore,a comparative analysis of education informatization policies in China and the U.S., sorting out the future direction and trends of international education informatization, and drawing on the rich experience of foreign education informatization development planning policies can provide important references for the strategy formulation and practical development of education informatization in China.
Volleyball Sports Teaching Based on Augmented Reality and Wireless Communication Assistance

The 21st century is the information age. The ever-changing information technology not only affects the development of the global economy, but also has an important impact on the field of education and personnel training. Under the influence of information technology, the way people acquire, impart, and evaluate knowledge has changed greatly. The educational concepts, models, and methods of the new era are fundamentally different from traditional education, which provides new opportunities and new challenges for the education and teaching work of contemporary teachers. This paper aims to discuss the application of augmented reality technology in volleyball teaching, and give an effective method for AR to be applied to volleyball teaching. In this paper, the existing AR teaching system has the problem of poor transmission rate and cannot cooperate with volleyball teaching well. Therefore, a wireless communication auxiliary algorithm based on intelligent reflective surface is proposed. And the current situation of volleyball teaching is investigated and analyzed. And in response to the survey results, 78% of the students supported teachers in applying AR technology to assist teaching in volleyball teaching classes. This fully shows that there is still a lot of application space for AR technology in current volleyball teaching.
A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem

We propose a new approach to generating synthetic instances in the knapsack domain in order to fill an instance-space. The method uses a novelty-search algorithm to search for instances that are diverse with respect to a feature-space but also elicit discriminatory performance from a set of target solvers. We demonstrate that a single run of the algorithm per target solver provides discriminatory instances and broad coverage of the feature-space. Furthermore, the instances also show diversity within the performance-space, despite the fact this is not explicitly evolved for, i.e. for a given 'winning solver', the magnitude of the performance-gap between it and other solvers varies across a wide-range. The method therefore provides a rich instance-space which can be used to analyse algorithm strengths/weaknesses, conduct algorithm-selection or construct a portfolio solver.
Mathematical Methods on Optimal Frequency Selection Model for Wireless Communication Transmission Signal under 5G Internet of Everything

Adaptive 5G communication has been accelerated thanks to the catalytic advances in the stable Internet of Things (IoT) which is a growing industry. This essay examines the impact of IoT, describes the evolution of the Internet under beneficial 5G communications, and explores 5G adaptive exchange and coordinating IoT advances. The reproduction evaluation was compatible with the evolution of telephone breathing, and developments in base station rigidity lowered base station energy consumption and club energy under the specification of ensuring communication quality, which means to further reassure compatibility. According to research, in a thick and uniform client course condition, power consumption has dwindled due to the extension of force use, but it has fundamentally improved and the correspondence quality has increased beyond what many would imagine achievable. It can ensure the client’s reasonableness and plan ability to execute, as well as fulfill the rate requirements of a variety of clients. The quick outcomes greatly test the speculative accuracy examination.
Application of Face Recognition Technology in Intelligent Education Management in Colleges and Universities

The application of artificial intelligence technology can help colleges and universities effectively improve the efficiency of campus management and teaching quality. The traditional educational management concepts and models in colleges and universities can no longer meet the growing needs of students. In this paper, the application of artificial intelligence technology in intelligent education management in colleges and universities is proposed. The face recognition technology is constructed through the volume neural network model and the hog algorithm. The experimental results show that the face recognition system can meet the real-time requirements of face recognition and improve the accuracy of face recognition. In addition, in the dormitory management application experiment, the face recognition system proposed in this paper has more advantages than the traditional algorithm in recognition accuracy. The face recognition system in this paper can identify the problems existing in class attendance and provides a certain reference value for the classroom on campus.
A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network

Teaching quality evaluation (TQE) is an important link in the process of school teaching management. Evaluation indicators and teaching quality have a complicated and nonlinear connection that is influenced by several variables. Some of these drawbacks include too much subjectivity and unpredictability, difficulty in defining index weights, sluggish convergence, and weak computer capacity. The current assessment techniques and models have these issues as well. This research uses an ANN model to assess the quality of mathematics instruction at colleges and universities (CAU) in order to address the challenging nonlinear issue of TQE and completes the following tasks. (1) The background and significance of TQE research are analyzed, and the domestic and foreign research status of TQE and neural network is systematically expounded. (2) The technical principle of DNN is introduced and the DDAE-SVR DNN model is constructed, and then, the evaluation index system of mathematics teaching quality is constructed. (3) The DDAE-SVR DNN model is put out as a potential alternative. The Adam method is used in the unsupervised training process to dynamically modify the learning step size for each training parameter. The spatial properties of the original data may be modified several times such that the reconstruction can be completed after many hidden layers have been applied. Data essentials such as precision, accuracy, and consistency are prioritized above all other considerations when generating the final product. Unsupervised prediction uses SVR and maps the complicated nonlinear connection into high-dimensional space in order to attain linearity in low-dimensional space, which is the goal of the supervised prediction process. The usefulness and benefits of the model provided in this research in the mathematics TQE may be verified by tests including the use of the TQE dataset and comparisons with other shallow models.
CoCoA: conditional correlation models with association size

Summary Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed–accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose a conditional correlation model with association size, a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semiparametric estimators adapted from genomic studies and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed–accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modeling and partitioned correlation analyses.
Evaluating Differences in Mathematical Competencies in Middle School Students during Pandemic Conditions through Preparatec Platform

This research proposes to evaluate the difference in essential mathematical competencies of middle school students transitioning to high school level under pandemic conditions (COVID-19) using the PreparaTec platform and Programme for International Student Assessment (PISA) released items as an evaluation instrument. The study was conducted with 84 students from first semester high school at Prepa Tec (Tecnologico de Monterrey, Campus Cuernavaca) aged 14 and 15. The students were divided into three experimental groups and a control group. Three levels of mathematical competencies: reproduction, definitions, and calculations (level 1), connections and integration to solve problems (level 2), and reflection (level 3) were evaluated. We applied a pre-test at the beginning of the semester and a post-test at the end after using the PreparaTec platform. Both tests were written and applied in a face-to-face format. The study presents the findings regarding the impact of using the PreparaTec platform during the first semester in the three levels of mathematical competencies mentioned. The results obtained are presented in two analyses. The first analysis involves the results obtained per experimental group and according to the number of correct answers per group. The second analysis represents a comparison between the percentage of correct answers and the level of difficulty per question per student regarding the percentage, determined by the Organisation for Economic Cooperation and Development (OECD) and Spain in the format presented by PISA. The pre-test and post-test consisted of 23 questions from items released from PISA, which contemplated measuring the three levels of mathematical competence. The results showed an improvement of 57% in level 1 proficiency questions, 63.6% in level 2 proficiency, and 100% in level 3 proficiency. The findings indicate that new teaching strategies based on Information and Communication Technologies (ICT) and more meaningful assessment forms further develop students’ mathematical competencies.
Temporal Factorized Bilinear Modules with 2D CNN for Action Recognition in Videos

Action recognition is to automatically detect and classify human&#x2019;s action in videos, with difficulty lies in modeling temporal relationship between frame sequences. The well-used 2D convolution neural network (CNN) is not suitable for this work, due to lacking temporal modeling ability. In this paper, a novel 2D CNN with inter frame information extraction module based on bilinear operation is proposed to deal with this problem. This model can greatly improve the temporal modeling ability of 2D CNN and just introduce a small amount of storage and calculation via parameter decomposition method. In addition, it has a flexible form to easily make tradeoff between performance and complexity. Finally, the effectiveness of this new network is validated on two kinds of benchmarks including both temporal-related (Something-Something v1) and scene-related(mini-kinetics), with top-1 accuracy 44.5% and 67.8% respectively, which reach or exceed the performance of existing methods with the similar model complexity.
Design of Question Answering Interactive Open Platform for Power Grid Business Acceptance Robot

In order to improve the matching degree between answers and questions of the power grid business acceptance robot question answering platform, an interactive open platform for power grid business acceptance robot question answering is designed. Firstly, an open platform of questions and answers interaction with data layer and information retrieval layer as the main body is constructed. To improve the question and answer interactive open platform database, the data layer is divided into three modules: data collecting, database setup, and data storage. At the same time, the information retrieval layer is divided into user input, answer display, query expansion, and information retrieval modules. To improve the retrieval accuracy, the corresponding answers are matched by using the similarity between questions and documents in the information retrieval module, and finally, the design of the question and answer interactive open platform of power grid business acceptance robot is realized. The experimental results demonstrate that the designed platform’s service acceptance accuracy is high, the AUC curve is low, and the degree of matching between replies and queries is high.
Comparing the quantum switch and its simulations with energetically constrained operations

Quantum mechanics allows processes to be superposed, leading to a genuinely quantum lack of causal structure. For example, the process known as the quantum switch applies two operations $\mathcal{A}$ and $\mathcal{B}$ in a superposition of the two possible orders, $\mathcal{A}$ before $\mathcal{B}$ and $\mathcal{B}$ before $\mathcal{A}$. Experimental implementations of the quantum switch have been challenged by some on the grounds that the operations $\mathcal{A}$ and $\mathcal{B}$ were implemented more than once, thereby simulating indefinite causal order rather than actually implementing it. Motivated by this debate, we consider a situation in which the quantum operations are physically described by a light-matter interaction model. While for our model the two processes are indistinguishable in the infinite-energy regime, restricting the energy available for the implementation of the operations introduces imperfections, which allow one to distinguish processes using different numbers of operations. We consider such an energetically constrained scenario and compare the quantum switch to one of its natural simulations, where each operation is implemented twice. Considering a commuting-versus-anticommuting unitary discrimination task, we find that within our model the quantum switch performs better, for some fixed amount of energy, than its simulation. In addition to the known computational or communication advantages of causal superpositions, our work raises questions about their potential energetic advantages.
Influence of Cloud Computing Adaption on Organization Performance: A Case Study of Selected Commercial Banks in Ilala Municipality

This study focuses on the influence of cloud computing adaption on organization performance with particular reference to selected Commercial Banks in Ilala Municipality. Specifically, the study aimed to assess how cloud computing increases efficiency of bank operations, to examine the effectiveness of cloud computing on enhancing customer satisfaction and to identify the challenges and opportunities encountering Commercial Banks in adopting and using cloud computing services. A sample of 70 respondents was drawn from the study population using simple random and purposive sampling techniques. Research questionnaire was applied in data collection and the collected data were analyzed using quantitative research techniques. Frequencies and percentages were computed to establish descriptive information of the collected data. Multiple regression statistical analysis with the help of Statistical Package for Social Science (SPSS) was employed to analyse the collected data. Results revealed that cloud computing is easy to use in banking operations, it shows that cloud computing in banks is accurate in data processing whereby to improve efficiency cloud technology can bring together multiple data and operational systems and increase efficiency in bank operations. Basing on the findings the study recommends that cloud computing service providers solve the issues that hamper the adoption of cloud computing. The government should create policies on cloud computing to be taught from schools so that people become aware of cloud computing. Seminars and events should be organized so that different kinds of individuals can be educated and informed about different ideas concerning cloud computing adaptation. The study recommends for further studies that cloud computing is a rapidly emerging technology in many companies. It would be interesting to concentrate on other industries that may have a greater demand for information management and so need the usage of cloud computing.
Mathematical Model Construction of Teaching Evaluation in Colleges and Universities Based on Convolutional Neural Network under the Background of Big Data

With the development of teacher evaluation, the main purposes of university evaluation are divided into the final purpose of ensuring teaching quality and the formative purpose of promoting professional learning. In order to effectively help teachers teach students according to their aptitude and realize personalized learning. This paper mainly analyzes and studies the mathematical model of college teaching evaluation based on convolutional neural network under the background of big data. The research analyzes the teaching evaluation under the background of big data and evaluates the teaching of big data participants. The teaching evaluation data can promote the development of education and teaching. Data acquisition processes and functions in the context of big data. Ensure the comprehensiveness and accuracy of data collection. At the same time, the function of teaching evaluation results is analyzed. The evaluation and formulation of mathematical model program in this study make full use of education big data information to provide scientific support for education decision-making.
Examine the Mediating Role of the Information Technology Capabilities on the Relationship Between Artificial Intelligence and Competitive Advantage During the COVID-19 Pandemic

This research aims to delve into the mediating role of IT capabilities in the relationship between artificial intelligence and competitive advantage during the COVID-19 pandemic. The study was conducted on a sample of 224 individuals from e-commerce enterprises in Jordan. Smart PLS3 was employed to examine the information gathered to inform the research goal. The result of this study confirmed that the adoption of IT capabilities plays a mediator role between artificial intelligence and competitive advantage. The results of the study have implications for decision-makers as well as the current literature. Companies that adopted IT capabilities and artificial intelligence grasp the benefits of successfully overcoming the COVID-19 Pandemic challenges.