Reviewerly/exHarmony huggingface dataset:
Conception of a Machine Learning Driven Adaptive Learning Environment Using Three-Model Architecture

Machine learning-based adaptive learning environments adapt in real time to users and their learning state. Information processing of adaptive learning environments can be based on different models. This research work deals with the practical implementation of a three-model architecture in an AI-based learning environment. It investigates the didactic requirements for every component of the model, the techniques that can be used to design and implement the components in an adaptive learning environment, and the challenges that arise. The tools and didactical concepts to be selected represent the current state of development. The focus of this paper is to demonstrate simple strategies for conceptualizing adaptive learning environments so that future creators of these environments, including media didacticians, educators, and developers of modern educational technologies, can independently design their own adaptive learning environments.
Optimistic Exploration Based on Categorical-DQN for Cooperative Markov Games

In multiagent reinforcement learning (MARL), independent cooperative learners face numerous challenges when learning the optimal joint policy, such as non-stationarity, stochasticity, and relative over-generalization problems. To achieve multiagent coordination and collaboration, a number of works designed heuristic experience replay mechanisms based on the ‘optimistic’ principle. However, it is difficult to evaluate the quality of an experience effectively, different treatments of experience may lead to overfitting and be prone to converge to sub-optimal policies. In this paper, we propose a new method named optimistic exploration categorical DQN (OE-CDQN) to apply the ‘optimistic’ principle to the action exploration process rather than in the network training process, to bias the probability of choosing an action with the frequency of receiving the maximum reward for that action. OE-CDQN is a combination of the ‘optimistic’ principle and CDQN, using an ‘optimistic’ re-weight function on the distributional value output of the CDQN network. The effectiveness of OE-CDQN is experimentally demonstrated on two well-designed games, i.e., the CMOTP game and a cooperative version of the boat problem which confronts ILs with all the pathologies mentioned above. Experimental results show that OE-CDQN outperforms state-of-the-art independent cooperative methods in terms of both learned return and algorithm robustness.
Benchmark of eight commercial solutions for deterministic intra-day solar forecast

Continuous increase of the production of photovoltaic energy requires precise forecasting to ensure grid stability. This paper presents a detailed benchmark of eight commercial forecasting solutions for intra-day solar forecasts. The comparison was carried out on a period of six months, from November to May 2021, on seven different PV plants located in different countries of the northern hemisphere. Performance evaluation metrics MAE, RMSE and MBE are used in order to analyze the forecasting precision. It is shown that forecasting solar power remains challenging, as shown by the important dispersion between the actors that we have observed.
Dynamic decoupling for multi-level systems

&lt;sec&gt;Dynamical decoupling refers to a family of techniques that are widely used to suppress decoherence in various quantum systems, caused by quasi-static environmental noise. They have broad applications in the field of quantum information processing. Conventional dynamical decoupling targets at noise in two-level system such as qubits and often consists of specifically engineered sequences of &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \pi $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-20222398_M1.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-20222398_M1.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; pulses that swap between two different states. On the other hand, researchers do not limit their study within simple two-levels systems any more, but go and seek for even more efficient quantum hardware. A variety of quantum algorithms and schemes of quantum control using multi-level systems, such as qutrits and qudits, for quantum information processing have been proposed and implemented successfully. However, decoherence in such a multi-level system is inherently more sophisticated than that in two-level systems. So far there has been little systematic research on how to tackle decoherence problems in such systems.&lt;/sec&gt;&lt;sec&gt;In this work, we propose several sequences of dynamical decoupling for multi-level systems that only rely on &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \pi $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-20222398_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-20222398_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; pulses linking neighboring levels, which is easy to implement experimentally. Our results show that these sequences can efficiently suppress quasi-static noise presented in multi-level systems. In addition, by calculating the corresponding filter functions of these sequences, we are able to further analyze their effect on generic Gaussian noise that may not be quasi-static. We also give a physical explanation of the noise filtering mechanism of these sequences by considering their control functions. Other topics discussed in our work include power spectral density and correlation of noise in multi-level systems. Our work may be regarded as a first step towards a more systematic investigation of dynamical decoupling techniques applicable to multi-level systems.&lt;/sec&gt;
DEVELOPMENT OF INNOVATIVE FORMS OF MENTORING IN MODERN PEDAGOGICAL ACTIVITY

The relevance of this study is due to the need to search for new forms of human development in the context of globalization, one of which is mentoring. The article shows the importance of this form of interaction with students as the most popular type of educational activity in modern pedagogy. The purpose of the article is to describe and disclose the best forms of mentoring using the method of comparative analysis, and to identify possible types of effective cooperation between teachers and students in a modern university. A brief description of the history of this type of pedagogical activity is given, as well as various forms of mentoring implemented within the framework of the pedagogical process are considered. There are five main types of mentoring that are actively used in practice at the pedagogical university. Both classical forms of mentoring, in which a more experienced and senior specialist transmits his knowledge to students, and innovative forms of interaction with students, which imply several levels of mentoring, as well as inverse types of cooperation, implying the work of a student as a mentor for a teacher, are considered. Special attention is paid to the importance of the mentor's control over the results of educational activities and the possibility of correcting mentoring activities. According to the authors of the article, mentoring has a high potential for further development and will allow achieving high results of pedagogical activity in educational organizations. The authors propose to update the principles of regulatory regulation of the institute of mentoring together with the methodological and pedagogical foundations of such a form of work with students, which has a high potential for practical application.
Remote Tutoring System of Ideological and Political Course Based on Mobile Client

Long-distance tutoring system can not quickly from the massive learning resources in search of users interested in resources. Therefore, this paper puts forward the design of distance tutoring system based on mobile client. In the hardware part, the FPGA ARM framework is adopted, and the XGMII interface is connected with the harmonic sub-layer of the link layer to realize the continuous transmission of a large number of data streams.In the software part, the interactive structure of the system is designed to form a good docking and interactive relationship between online learning and after-class learning mode. Design module structure based on mobile client, to meet the needs of students’ online VOD courseware, curriculum information and teacher information. Based on the hybrid recommendation algorithm, the personalized course recommendation is carried out, and the courses are retrieved from the relevant course recommendation database and returned to users. System test results show that the system designed in this paper can reduce the maximum response time of user query and processing applications, so it is more practical.
Risk Identification Model of Enterprise Strategic Financing Based on Online Learning

The current financing risk identification model only identifies according to the financial situation of enterprises. The limitations of risk indicators affect the accuracy of identification, and have great limitations for enterprises in different industries. In view of the above problems, this paper will study and build an enterprise strategic financing risk identification model based on online learning. After analyzing the causes of enterprise strategic financing risk, the financing risk identification index system is designed. Based on the structure of impulse neural network, the risk identification model is constructed by using online learning algorithm. The model test results show that the minimum identification accuracy of the model is 92.4%, which has a good risk identification effect for enterprises in different industries.
Research on Operational Risk Monitoring Method of Intelligent Financial System Based on Deep Learning and Improved RPA

The accuracy of traditional financial system operational risk monitoring is low. Therefore, this paper proposes a method of intelligent financial system operational risk monitoring based on deep learning and improved RPA. Set the financial monitoring index and obtain the warning threshold parameters; Using deep neural network method to mine key risk indicators and obtain reconstruction coefficients of data mining errors of financial system; By improving the RPA method to calculate the fit degree of financial risk, it matches the internal business process of the enterprise; The operational risk monitoring algorithm of financial system is designed to realize the operational risk monitoring of financial system. The experimental results show that the risk monitoring accuracy of the design method is 80.3%, and the overall test threshold of the model is 0.5 after the introduction of non-financial indicators, which shows that it can be applied in practice.
Prediction Method of Crack Depth of Concrete Building Components Based on Ultrasonic Signal

Cracks in concrete components have a serious impact on the safety of building structures. On the one hand, with the increase of service life, cracks will reduce the safety of building structures, and with the deepening of cracks, the service life of building structures will be reduced. Therefore, it is very necessary to predict cracks in concrete components. In the crack prediction of concrete building components, the predicted results deviate from the actual value due to the deviation of the measured strain value of concrete. Based on ultrasonic signal, a method for predicting crack depth of concrete building components is proposed. In the process of concrete ultrasonic transmission, the number and length of micro cracks will increase and expand due to stress concentration. According to this phenomenon, the finite element simulation of concrete building components is carried out to obtain the damage model. The characteristics of concrete cracks are extracted based on ultrasonic signals, and the law of acoustic frequency changing with time can also reflect the state of medium stress. The extracted crack signal features of concrete building components are input into CNN model for prediction and recognition. The test results show that the prediction method of crack depth of concrete building components based on ultrasonic signal can improve the accuracy of prediction results and has high engineering application value.
Personalized Recommendation Method of Nursing Multimedia Teaching Resources Based on Mobile Learning

Due to the variety and quantity of nursing multimedia teaching resources, the resource recommendation method has the problem of low recall rate. To this end, a mobile learning-based nursing multimedia teaching resource recommendation method was designed. First of all, this paper identifies the law of learning needs, annotates the keywords of teaching resources, and collects the data of students’ learning records, so as to improve the recall rate of the recommendation results. Build a user interest preference model, improve the nursing multimedia teaching resource recommendation process, and optimize the mobile learning personalized recommendation model. The experimental results show that the recall rates of the proposed method and the other two methods are 78.627%, 70.615% and 70.200%, respectively, indicating that the proposed method has a high recall rate.
Construction of Mobile Education Platform for Entrepreneurial Courses of Economic Management Specialty Based on Cloud Computing

In order to solve the problem of unbalanced course resource scheduling of mobile education platform when the number of users increases, a mobile education platform based on cloud computing is constructed. In the hardware part, the FPGA chip of XC6SLX16 is selected as the platform, and the decoupling network is designed according to different power input to eliminate the noise on power pin. In the software part, according to the requirements of the economic management course, we integrate the scattered teaching to form the rich teaching resource base and realize the unified management of users, roles and organizations. In order to improve the concurrency performance of the platform, the entrepreneurial course resource database of economic management major is scheduled based on cloud computing. Design each function module of the mobile education platform, input the keywords can get the related more detailed development resources, realize the mutual communication discussion. The test results show that the platform has good performance, and can improve the network throughput and meet the design requirements.
A Method of Abnormal Psychological Recognition for Students in Mobile Physical Education Based on Data Mining

At present, the abnormal psychological recognition of middle school students is mainly through psychological questionnaire, after data statistical processing, to assess whether there is abnormal psychological students. The recognition accuracy is strongly dependent on the reliability of the questionnaire, which leads to the poor recognition accuracy and stability. In order to solve these problems, the method of abnormal psychological recognition of students in mobile PE teaching based on data mining will be studied. After analyzing the influence of PE teaching on students’ psychology, the behavioral characteristics that represent students’ psychology are extracted. After constructing the students’ psychological view, the students are classified preliminarily. Through constructing mental state mining decision tree, using iForest algorithm to realize abnormal mental recognition for middle school students. The test results of recognition method show that the accuracy of the mental recognition method is stable between 87.28% and 87.95%, and the recognition reliability is higher.
The Mobile Teaching Method of Law Course Based on Wireless Communication Technology

In order to better improve the teaching effect of law and improve the teaching efficiency of law courses, this paper proposes a mobile teaching method of law courses based on wireless communication technology. First, optimize the law teaching system, combine wireless communication technology to build a mobile teaching platform for law courses, optimize teaching content and teaching evaluation indicators, and achieve the goal of mobile teaching. The experimental results show that the design method can effectively improve the teaching efficiency of law courses, and has certain application value.
Compensation Method of Infrared Body Temperature Measurement Accuracy Under Mobile Monitoring Technology

Infrared human body temperature measurement is widely used in medicine because of its high safety and flexibility. But in the process of application, there is a big error with the actual temperature. Identify the environmental characteristics of infrared temperature field, judge the high and low temperature area, construct the nonuniformity correction model, convert the electrical signal to the temperature value of the target object, optimize the human body temperature measurement process, introduce the concept of brightness temperature, and use the moving monitoring technology to design the precision compensation mode. The experimental results show that the error mean of the infrared human body temperature compensation method and the other three methods is smaller, which shows that the performance of the method is better.
Personalized Recommendation Method of Online Music Teaching Resources Based on Mobile Terminal

Due to the large number of users of the mobile teaching terminal and the many types of music teaching resources, the recommendation accuracy is low. To this end, this paper proposes a personalized recommendation method for online music teaching resources based on mobile terminals. This paper identifies the characteristics of online music teaching resources, connects the resources through knowledge points, and optimizes the streaming media storage format using mobile terminals. The time continuous signal is converted into discrete time signal, and the user interest model is constructed by collaborative filtering, and the favorite resources of neighbor users are recommended to the current user. The experimental results show that the accuracy of this method is 75.694%, 66.669% and 66.350%, respectively, which shows that the performance of this method is better than the other two methods.
Design of Numerical Control Machining Simulation Teaching System Based on Mobile Terminal

Today’s manufacturing industries all over the world widely use CNC technology to improve the manufacturing capacity and level, and improve the adaptability and competitiveness of the dynamic and changeable market. The research and development of CNC technology and the promotion and application of CNC products require a large number of high-quality CNC professionals, and CNC teaching and training are therefore in a very important position. In order to improve the success rate of system requests, a numerical control machining simulation teaching system based on mobile terminals is designed. Using the combination of PC and motion control card, combined with the oscillation circuit inside the PIC16F877 microcontroller to form a complete oscillation circuit; using 3D graphics technology to simulate the CNC machining process, optimize the CNC machining process, build a tool database, and transfer these parameters to the In the simulation program, the cutting process is simplified as a one-dimensional Boolean operation along the line of sight, and the function of the simulation teaching system is designed by using the mobile terminal. Experimental results: The request success rate of the designed system is high, indicating that its use effect is better.
A Computational Time Analysis of Discrete Simulated Kalman Filter Optimizer

Simulated Kalman filter (SKF) is a population-based optimization algorithm based on the Kalman filter framework. To find the global optimum, the SKF applies a Kalman filter process that involves prediction, measurement, and estimation. However, the SKF can only operates in numerical search space. In literature, many techniques and modifications have been made to the SKF algorithms to function in a discrete search space. An example of the modified SKF is the discrete simulated Kalman filter optimizer (DSKFO). However, little research has been conducted on the DSKFO. This paper studies the computational time complexity of the DSKFO to acquire a better understanding of the algorithm's complexity. The analysis is done by comparing the computational time of the DSKFO against four combinatorial SKFs. The findings show that the DSKFO is the fastest algorithm for solving all TSP instances. The DSKFO requires just 13 s to solve the smaller TSP instance eil51, whereas SEDESKF, BSKF, DESKF, and AMSKF need 14, 34, 36, and 42 s, respectively. DSKFO solves the larger TSP instance dsj1000 in 79 s, whereas SEDESKF, BSKF, DESKF, and AMSKF need 182, 1104, 1125, and 1167 s, respectively.
Digital Management System of Library Books Based on Web Platform

In view of the slow response speed and single function of the library’s book management system, this paper designs a library’s digital management system based on the network platform. In the hardware part, t91sam9263 chip is used as the core, and the digital management system is established based on the web platform. In the software part, on the basis of dividing the grid space of digital management of books, the management mode of virtual space of books is designed by using virtualization technology. Introduce user interest indicators to recommend book resources personalized. The system test results show that in the face of high concurrent requests, the response time of the designed system is less than 300 ms, the error rate of book recommendation is less than 5%, and the performance is obviously improved.
Mobile Terminal-Oriented Real-Time Monitoring Method for Athletes’ Special Training Load

Aiming at the problem of inaccurate training load monitoring results due to the large individual differences of athletes’ special training, a real-time monitoring method for athletes’ special training load for mobile terminals is proposed. Use sensors as data collection devices to establish sports scenarios for mobile terminals, and use smart wearable devices to collect real-time data generated by special sports measured by sensors embedded in watches. Select the index reflecting the intensity of training load to find the regularity of athletes’ training growth. By recording the time ratios of different heart rate intervals for each exercise, the distribution and variation of the load intensity during the training were analyzed. Capture complete training load data during the movement. Through the real-time monitoring and early warning of special training load, the training status and training load of athletes can be adjusted, which is beneficial to improve the training quality of athletes. The test results show that the proposed method can improve the monitoring accuracy and provide a reference for improving the project training program.