Reviewerly/exHarmony huggingface dataset:
Mining Algorithm of Massive Online Financial Education Resources Based on Apriori TIDS

Online financial education resources are massive. Traditional algorithms are affected by the load of resource processing nodes, resulting in low accuracy and long mining time. Therefore, a massive online financial education resource mining algorithm based on Apriori TIDs is proposed. The Apriori TIDS algorithm is used to establish the characteristic equation of massive online financial education resources. By extracting the number of principal factors of the characteristic vector, the proportion of financial education resources in each dimension of each resource processing node in its total resources is calculated, so as to obtain the residual value of financial education resources of the resource processing node, and calculate the dynamic weight of financial education resources based on this, The residual load capacity of resource processing nodes is obtained. Combined with the design of massive online financial education resource mining algorithm, the mining of massive online financial education resources is realized. Experimental results show that the proposed algorithm can not only improve the accuracy of mining, but also shorten the mining time and have better mining performance.
The Problems and Analysis of Artificial Intelligence Specialty Construction in Universities Under the Present Situation of Artificial Intelligence Development

China’s independent innovation ability in the field of artificial intelligence is a key link to occupy the commanding heights of future science and technology and talent competition. The cultivation of artificial intelligent talents is crucial to promote the development of the artificial intelligent industry. There is a large demand for artificial intelligence talents in the market, but the development of artificial intelligence in colleges and universities is still faced with many problems, such as the construction of related disciplines and majors has just started, the strength of artificial intelligence teachers in colleges and universities is weak, the talent training is mainly based on postgraduate education and the education system is not perfect. This paper analyzes the current cultivation situation of artificial intelligence major in undergraduate colleges and vocational colleges, points out the shortcomings in the construction of artificial intelligence major, and gives some suggestions.
Intelligent Education Information Resource Integration and Sharing System Based on Cloud Computing

In order to improve the integrated storage capacity and sharing security of educational information resources, this paper proposes and designs an intelligent educational information resources integration and sharing system based on cloud computing. The system hardware is composed of user management module, education resource management module, related education resource system integration module and system management module. The curriculum resource management module is designed with b/s architecture, namely browser/server structure. In short, the b/s structure can be divided into three levels: client software, application server, database server, etc. Through user management, educational resource management and related educational resource system integration, software research is realized. The experimental results show that the intelligent education information resources integration and sharing system based on cloud computing can effectively improve the storage capacity and the security of sharing.
Research on English Translator Speech Recognition System Based on Deep Learning

The application of English translators is affected by speech recognition technology. Current speech recognition systems use Hidden Markov Models for recognition, which are susceptible to interference from noise and the magnitude of the recognition object, resulting in low accuracy and efficiency of the recognition system. Aiming at the above problems, research and design an English translator speech recognition system based on deep learning. Design the system hardware support software function with the combination of FPGA and STM32F4 microprocessor as the core. After preprocessing the speech sequence collected by the English translator and extracting the features, the DNN neural network trained by the restricted Boltzmann machine is used to recognize the speech sequence features to realize the speech recognition function. In the experiment, the DNN neural network has better recognition performance than the HMM model. The designed recognition system takes an average of 23.5 ms to recognize and has a higher recognition efficiency.
Research on the Balanced Allocation Model of Online Education Resources of Economics and Management in Colleges and Universities Based on Parallel Clustering

In view of the poor effect of the current balanced allocation of online education resources, this study proposes a balanced allocation model of online education resources of economics and management in colleges and universities based on parallel clustering. Firstly, combined with the principle of parallel clustering, an online education resource database is constructed to collect, analyze and store a large number of economic management teaching resources. Then build the information management platform and optimize the evaluation index of balanced allocation of educational resources. Finally, the experiment proves that the model in this paper has high practicability in the practical application process and fully meets the research requirements.
An Online Vocal Music Teaching Timbre Evaluation Method Based on Feature Comparison

The traditional vocal music teaching model has been unable to meet the needs of today’s vocal music teaching, and the promotion and improvement of the online vocal music teaching model is imperative. Due to the influence of various factors (online equipment, environmental noise, etc.), there are certain defects in the timbre of online vocal music teaching, which cannot guarantee the effect of vocal music teaching. Preprocess the timbre signal of online vocal music teaching (remove mute segment, pre-emphasis and windowing), and based on this, extract timbre signal features (time domain feature, frequency domain feature and cepstral domain feature). In this paper, a feature comparison model of timbre signal is constructed to reduce the dimension of timbre signal feature vector. It adopts SAGA algorithm to determine the timbre evaluation formula of online vocal music teaching, and realizes the evaluation of timbre of online vocal music teaching. The experimental data show that compared with the reference standard, the complete rate of timbre signal feature extraction and the correct rate of timbre evaluation obtained by the proposed method are higher. The experimental results fully confirm the effectiveness and feasibility of the proposed method.
Evaluation Method of Oral English Digital Teaching Quality Based on Decision Tree Algorithm

In order to solve the shortcomings of low accuracy and long time-consuming in the evaluation process of traditional teaching effect evaluation methods, this paper applies the decision tree algorithm to this, and designs an evaluation method for the teaching effect of oral English teaching. Different from other methods, the method designed in this paper is to evaluate students and teachers. After the evaluation system is constructed, the decision tree algorithm is used to process the collected original data samples, and the data attribute classification rules are set, the evaluation score is formulated, and the final evaluation is obtained by synthesizing the evaluation results. The experiment proves that the designed evaluation method has high accuracy and short time-consuming, which proves that the designed evaluation method has good evaluation effect.
Real-Time Collection Method of Learning Status Data in Distance Teaching Based on Internet of Things

With the increase of distance learning data, the traditional methods have some problems, such as low efficiency and poor accuracy. Therefore, this paper studies the real-time collection method of learning state data of distance education based on Internet of things. Based on the analysis of the interrelated factors of distance learning status data, the different quadrants are divided according to the real-time of learning status data acquisition. On the basis of different collection schemes, the priority level of real-time collection of learning state data in distance education is identified. This paper combines the design of real-time collection algorithm of distance learning state data, and realizes real-time collection of distance learning state data. Experimental results show that the proposed method can improve the efficiency and accuracy of data acquisition in distance education.
Evaluation Method of Classroom Teaching Quality of Marxist Theory Course Based on Deep Learning

The traditional evaluation method of Marxist theory course has the problem of long evaluation time. Therefore, this paper proposes a evaluation method of Marxist Theory Course Based on in-depth learning. First of all, it analyzes the current situation of classroom teaching of Marxist theory course, designs the evaluation index of, determines the weight of evaluation index, extracts the characteristics of evaluation index by using deep learning algorithm, and constructs the evaluation system of Marxist Theory Course in combination with the principles of scientificity, systematization, independence and operability of index selection, According to the analytic hierarchy process, the weight of teaching quality evaluation index is calculated, and evaluation of Marxist theory course is realized. The experimental results show that this method can effectively shorten the time of evaluation and has better evaluation performance.
An Analysis on the Training Mode of Master Students in Artificial Intelligence Field for Electronic Information Professional Degree—Take Hunan Normal University as an Example

The ability of independent innovation in the field of artificial intelligence is a key element to occupy the commanding heights of future technology and talent competition in China. The cultivation of artificial intelligence talents, especially the cultivation high-end talent, it is essential to promote the development of artificial intelligence industry. At present, the market for artificial intelligence talents is in great demand. There is a shortage of high-end artificial intelligence talents, the weak strength of artificial intelligence teachers in universities, and the incomplete education system for talent training and many other issues. This paper combines some measures taken by Hunan Normal University for graduate students in the field of artificial intelligence in the degree of electronic information, the necessity and feasibility of artificial intelligence training senior talents are discussed for social needs in the field of artificial intelligence, The training goals and methods of postgraduate students in the field of artificial intelligence under the professional category of electronic information major are clarified, and the construction of artificial intelligence field is planned.
Design of Multiple Interactive Sharing System for Electric Power Subject Course Resources

In the process of resource transmission, the existing multi-interactive sharing system does not take encryption measures, which leads to poor security and incomplete information transmission. Therefore, focusing on the teaching resources of electric power discipline, the optimization of multi-interactive sharing system is realized from three aspects: hardware, database and software. Improve the communication network structure, modify the teaching course resource collector, microprocessor, resource memory, collector and other equipment components, and complete the hardware system optimization through circuit modification and connection. A systematic database of teaching resources of electric power specialty is established. Constrained by the multi-interactive sharing protocol, the course resources of electric power discipline are compressed and added to the transmission queue by data encryption to complete the uploading task. After searching and downloading resources, the system finally realized multiple interactive sharing functions. Through the system test experiment, the loss rate of shared packets is less than 2% of the total resources, and the sharing time is less than 8000 ms.
Research on the In-Depth Recommendation Method of Grammar Topics for English Online Teaching

In the process of grammatical topic recommendation, affected by the dimension of word vector, there is a problem of low recommendation accuracy. To solve this problem, a deep recommendation method of grammar topics for English online teaching is designed. Based on the classification theory of learning outcomes, the dimensions of students’ learning styles in English online classrooms were measured. It can accurately identify the characteristics of English grammar teaching and stimulate students’ interest in learning. The DeepFM model is constructed by using English online teaching technology, the parallel parameters are optimized, and the deep recommendation mode of grammar topics is set. Experimental results: The accuracy rates of the proposed method for in-depth recommendation of grammar topics for English online teaching and the other two methods for in-depth recommendation of grammar topics are: 73.183%, 63.205%, and 63.258%, respectively. The experimental results show that the accuracy of the in-depth recommendation method of grammar topics has been improved after fully combining the English online teaching technology.
Research on Effect Evaluation Method of Ideological and Political Classroom Teaching Reform in Colleges and Universities Based on Particle Swarm Optimization Algorithm

In order to improve the teaching effect and improve the accuracy of the evaluation method of the reform effect, this paper introduces particle swarm optimization algorithm to realize the effect analysis of the ideological and political teaching reform. First, define the evaluation data file, build a reasonable reform evaluation system, obtain the relevant characteristics of user interests, mine user preferences, design the reform evaluation extraction function, determine the evaluation content according to the system, and complete the reform effect evaluation using particle swarm optimization algorithm. The experimental results verify the accuracy and reliability of this method in the reform evaluation process, which is of great significance.
Research on Teaching Effect Evaluation of Innovation and Entrepreneurship Based on Collaborative Filtering Algorithm

Entrepreneurship plays an important role in economic development and social progress. Innovation and entrepreneurship teaching for college students is an important part of cultivating innovative talents. However, the evaluation of the teaching effect of innovation and entrepreneurship is not comprehensive enough. Therefore, based on collaborative filtering algorithm, an evaluation method of the teaching effect of innovation and entrepreneurship is designed. Firstly, according to the teaching practice, the evaluation index of innovation and entrepreneurship teaching effect is selected to comprehensively determine the teaching effect. On this basis, the evaluation model of innovation and entrepreneurship teaching effect based on collaborative filtering algorithm is constructed. The experiment shows that the designed evaluation method of innovation and entrepreneurship teaching effect is accurate, with an average error of 0.03%, which has certain application value.
Does Catastrophic Forgetting Negatively Affect Financial Predictions?

Nowadays, financial markets produce a large amount of data, in the form of historical time series, which quantitative researchers have recently attempted at predicting with deep learning models. These models are constantly updated with new incoming data in an online fashion. However, artificial neural networks tend to exhibit poor adaptability, fitting the last seen trends, without keeping the information from the previous ones. Continual learning studies this problem, called catastrophic forgetting, to preserve the knowledge acquired in the past and exploiting it for learning new trends. This paper evaluates and highlights continual learning techniques applied to financial historical time series in a context of binary classification (upward or downward trend). The main state-of-the-art algorithms have been evaluated with data derived from a practical scenario, highlighting how the application of continual learning techniques allows for better performance in the financial field against conventional online approaches (Code is available at https://github.com/albertozurli/cl_timeseries .).
Quantum Graph Realization of Transmission Problems

In this study, the relations between boundary value transmission problems and quantum graphs are explained. Firstly, a boundary value transmission problem with one point of interaction is considered and realized as a boundary value problem on a metric graph (two-star graph). The self-adjointness of the corresponding operators is discussed in these two settings. Secondly, a two-star graph with general self-adjoint vertex conditions is considered and treated as a transmission problem. To this end, a new special inner product which makes the quantum two-star graph self-adjoint in a general sense is defined. It is shown that the usual self-adjointness of a two-star graph can be obtained as a special case.
The Effect (Impact) of Project-Based Learning Through Augmented Reality on Higher Math Classes

Mathematics is essential in training engineers. A weak mastery in this area can affect other subjects that require math skills. Students’ lack of motivation towards mathematics influences their poor academic performance. It is necessary that the teaching of Mathematics be more creative and stimulating, considering modern society and the interests of students. Project-based learning (PBL) can help. In this paper, PBL is presented to qualitatively assess its impact on student motivation and performance. In a Calculus I class for Mechanical Engineering course, students developed a project using augmented reality. Applications for 3D object modelling and integral calculus were implemented. Augmented reality was exposed with a focus on the competencies considered in the learning project. The interactions established between students and professor were analysed through direct observation, document analysis and satisfaction questionnaires. Through the analysis it was possible to conclude that the experience proved to be effective and productive.
Local Optimisation of Nyström Samples Through Stochastic Gradient Descent

Abstract We study a relaxed version of the column-sampling problem for the Nyström approximation of kernel matrices, where approximations are defined from multisets of landmark points in the ambient space; such multisets are referred to as Nyström samples. We consider an unweighted variation of the radial squared-kernel discrepancy (SKD) criterion as a surrogate for the classical criteria used to assess the Nyström approximation accuracy; in this setting, we discuss how Nyström samples can be efficiently optimised through stochastic gradient descent. We perform numerical experiments which demonstrate that the local minimisation of the radial SKD yields Nyström samples with improved Nyström approximation accuracy in terms of trace, Frobenius and spectral norms.
Maximally Entangled Two-Qutrit Quantum Information States and De Gua’s Theorem for Tetrahedron

Geometric relations between separable and entangled two-qubit and two-qutrit quantum information states are studied. For two qubit states a relation between reduced density matrix and the concurrence allows us to characterize entanglement by double area of a parallelogram, expressed by determinant of the complex Hermitian inner product metric. We find similar relation in the case of generic two-qutrit state, where the concurrence is expressed by sum of all $$2 \times 2$$ minors of $$3\times 3$$ complex matrix. We show that for maximally entangled two-retrit state this relation is just De Gua’s theorem or a three-dimensional analog of the Pythagorean theorem for triorthogonal tetrahedron areas. Generalizations of our results for arbitrary two-qudit states are discussed
MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models

Imputing missing data is still a challenge for mixed datasets containing variables of different nature such as continuous, count, ordinal, categorical, and binary variables. The recently introduced Mixed Deep Gaussian Mixture Models (MDGMM) explicitly handle such different variable types. MDGMMs learn continuous and low dimensional representations of mixed datasets that capture the inter-variable dependence structure. We propose a model inversion that uses the learned latent representation and maps it with the observed parts of the signal. Latent areas of interest are identified for each missing value using an optimization method and synthetic imputation values are drawn. This new procedure is called MI2AMI (Missing data Imputation using MIxed deep GAussian MIxture models). The approach is tested against state-of-the-art mixed data imputation algorithms based on chained equations, Random Forests, k-Nearest Neighbours, and Generative Adversarial Networks. Two missing values designs were tested, namely the Missing Completly at Random (MCAR) and Missing at Random (MAR) designs, with missing value rates ranging from 10% to 30%.
FFD Augmentor: Towards Few-Shot Oracle Character Recognition from Scratch

Recognizing oracle characters, the earliest hieroglyph discovered in China, is recently addressed with more and more attention. Due to the difficulty of collecting labeled data, recognizing oracle characters is naturally a Few-Shot Learning (FSL) problem, which aims to tackle the learning problem with only one or a few training data. Most current FSL methods assume a disjoint but related big dataset can be utilized such that one can transfer the related knowledge to the few-shot case. However, unlike common phonetic words like English letters, oracle bone inscriptions are composed of radicals representing graphic symbols. Furthermore, as time goes, the graphic symbols to represent specific objects were significantly changed. Hence we can hardly find plenty of prior knowledge to learn without negative transfer. Another perspective to solve this problem is to use data augmentation algorithms to directly enlarge the size of training data to help the training of deep models. But popular augment strategies, such as dividing the characters into stroke sequences, break the orthographic units of Chinese characters and destroy the semantic information. Thus simply adding noise to strokes perform weakly in enhancing the learning capacity. To solve these issues, we in this paper propose a new data augmentation algorithm for oracle characters such that (1) it will introduce informative diversity for the training data while alleviating the loss of semantics; (2) with this data augmentation algorithm, we can train the few-shot model from scratch without pre-training and still get a powerful recognition model with superior performance to models pre-trained with a large dataset. Specifically, our data augmentation algorithm includes a B-spline free form deformation method to randomly distort the strokes of characters but maintain the overall structures. We generate 20–40 augmented images for each training data and use this augmented training set to train a deep neural network model in a standard pipeline. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our augmentor. Code and models are released in https://github.com/Hide-A-Pumpkin/FFDAugmentor .