Reviewerly/exHarmony huggingface dataset:
Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer

The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps. Since the finite features harvested from one single aesthetic style image are inadequate to represent the rich textures of the content natural image, existing techniques treat the full-channel style feature patches as simple signal tensors and create new style feature patches via signal-level fusion, which ignore the implicit diversities existed in style features and thus fail for generating better stylised results. In this paper, we propose a Retinex theory guided, channel-grouping based patch swap technique to solve the above challenges. Channel-grouping strategy groups the style feature maps into surface and texture channels, which prevents the winner-takes-all problem. Retinex theory based decomposition controls a more stable channel code rate generation. In addition, we provide complementary fusion and multi-scale generation strategy to prevent unexpected black area and overstylised results respectively. Experimental results demonstrate that the proposed method outperforms the existing techniques in providing more style-consistent textures while keeping the content fidelity.
A study on software development for comprehensive assessment of English online teaching quality in universities based on deep learning

Abstract In this paper, a deep learning ID3 algorithm based on a decision tree generation algorithm is proposed and applied to the comprehensive assessment of English online teaching quality. Secondly, the structure and design method of the data warehouse of the comprehensive assessment software of online teaching quality is also constructed, including the design of the operating system interface and the data warehouse itself. Finally, the application results and analysis of the English web-based comprehensive teaching quality assessment software were conducted through assessment attributes and network training. The results show that the correlation coefficient between the two is 0.9348 from the evaluation results of the sample and 0.9116 from the analysis of the network generalization performance test. The correlation coefficient between the two is close to completely accurate from the evaluation and test results. The software developed in this paper provides a certain reference value for achieving the improvement and optimization of teaching quality.
Considerations and Concerns of Professional Game Composers Regarding Artificially Intelligent Music Technology

Artificially intelligent music technology (AIMT) is a promising field with great potential for creating innovation in music. However, the considerations and concerns surrounding AI-generated music from the perspective of professional video game composers have yet to be fully explored. In this study, 11 professional video game composers were interviewed to determine how they feel about AIMT and how this informs future research and tool design within the games industry. The interviews were analyzed using a reflexive thematic analysis (RTA) to identify key themes. The study found that while composers recognize the benefits of music AI, they have complex concerns beyond the obvious concerns for AI infringing on their agency and creativity. There is an inherent clash between the creative ego and music AI, which can make it difficult for composers to embrace this technology. Furthermore, a lack of standard technical knowledge, support, understanding, and trust of music AI is impeding tool use within the industry. These findings have implications for music AI researchers and industry practitioners. By better understanding the concerns and considerations of professional creatives, researchers can design and communicate their tools more effectively to music professionals. Moreover, this study lays the foundation for empirical research into the relationship between professional creatives and emerging AI technology – a topic that is under-emphasised in current research.
Solar Radiation Analysis for Predicting Climate Change Using Deep Learning Techniques

Solar radiation, Earth's main energy source, affects surface radiation balance, hydrological cycles, plant photosynthesis, weather, and climatic extremes. A stacking model based on the best of 12 machine learning models predicted and compared daily and monthly sun radiation levels. The results suggest machine learning algorithms use climatic parameters. A trend study of high land surface temperatures and solar radiation showed how solar radiation compounds catastrophic climatic events. GBRT, XG Boost, GPR, and random forest models better predicted daily and monthly sun radiation. The stacking model, which comprises the GBRT, XG Boost, GPR, and random forest models, exceeded the single models in daily solar radiation prediction but did not outperform the XGBoost model in monthly prediction. Stacking and XG Boost models estimate sun radiation best.
Overview of the Round Table “University Management in Russia: Challenges and Solutions” within the Framework of the VII International Nevsky Forum

The article provides an overview of the round table “University Management in Russia: Challenges and Solutions”, which was held as part of the VII International Nevsky Forum “The Role of Russia in the Formation of a Multipolar World: Challenges, Opportunities, Prospects, Management Decisions”, held on June 21-23, 2023, in St. Petersburg based on the RANEPA. The round table was organized by the Department of Scientific and Information Development and Library Support and the Center for Advanced Social Research of the Institute of Social Sciences of the RANEPA as part of the Center for Interdisciplinary Research on Human Potential project, together with the Department of Scientific Work of the North-Western Institute of the RANEPA. The purpose of the round table was to search for effective managerial solutions to overcome such modern challenges by socio-humanitarian universities as: transformation of international relations and interactions; restriction of access to international databases of scientific information; the impossibility of achieving the strategic goals of Russia in the new realities with the help of old approaches.
Lovelace et al.’s 2005 poster about the WDC &lt;i&gt;Supersaurus&lt;/i&gt;

Back in 2005, three years before their paper on the WDC Supersaurus known as Jimbo was published, Lovelace at al. presented their work as a poster at the annual SVP meeting.
Analysis of the Economics of Talent Mobility Based on Logistic Model

Abstract This paper analyzes the relationship between talent introduction policy and talent mobility based on a logistic breakpoint regression model. The logit function and likelihood function are analyzed to determine the probability values between the relationship of things. The variable selection is achieved by adding penalty terms in estimating the regression parameters. Focused on the analysis of the MM algorithm to select the optimal control function and iterative solution to separate the parameters in the optimization problem. The processing effect of breakpoint analysis is studied, and breakpoint regression is classified and used to optimize the objective function to the minimum using local linear regression. The feasibility of the breakpoint analysis method is demonstrated by analyzing the use of breakpoint regression in different fields. The results surface: the probability of implementing a talent introduction policy for individuals on one side of the breakpoint is 0, while the probability on the other side of the breakpoint is 1, which meets the condition of exact breakpoint regression. The most talent policies are mainly concentrated in the third and fourth tier cities, accounting for 49%, followed by the second tier cities, with 39% of talent introduction policies.
An Innovative Discussion on Ideological and Political Education for College Students Based on Digital Mining

Abstract In this paper, a self-organized data mining model for college students’ ideological and political education is constructed. Firstly, a suitable input-output association set is specified to build the model, and then one or more are selected as the outer criterion to filter the model. The models of each layer are evaluated by the outer criterion, and the optimal complexity model is established by finding the model structure and the law of system operation through the selection of different models. Then unknown correlations between factors of ideological and political education were mined from the data samples, and the system variables were predicted and analyzed. Finally, the model is applied to analyze the effect of ideological education and the influencing factors of College A. The p-value of the degree of matching between education level and professional level is 0.456, and the education level will have a significant influence on ideological and political education. The research in this paper is important for the development of ideology and politics in colleges and universities.
Resource distribution of online and offline interactive teaching mode based on feature fusion network

Abstract This paper combines the text similarity calculation of co-occurring words, text similarity calculation of semantic similarity, text similarity calculation of cosine similarity, and text similarity calculation based on a language network and joins the feature fusion network to construct a multi-feature fusion text similarity calculation method, so as to realize the resource distribution of interactive teaching mode. Through the design of functional modules, database design, and framework design of the system, JavaEE technology is used to realize the system design and complete the online and offline interactive teaching. After testing, the distribution of teaching resources under this algorithm does not change as the number of users increases, and the range fluctuates between (0,25). The system can run stably when multiple classrooms and users are online at the same time, and the system resource consumption is around 95%, all in a reasonable range.
A Model for Integrating and Reconstructing Music Curriculum for Higher Education Based on Deep Learning

Abstract In this paper, firstly, the problem is modeled by a formal description of the problem of resource fragmentation in music courses for teachers, and the problem is simplified based on regional division. Secondly, an FFD-basic migration matching algorithm is proposed, and the migration matching of teaching resource nodes is realized by the least resource maximum flow model tuning. Then the measurement model of the music teaching ability of teachers was constructed, and the teaching ability measurement scale was designed. Finally, the effectiveness of the curriculum integration and reconstruction model was proved through empirical effect and analysis. The results show that the training speed is effectively increased by about 0.25 with the deep learning based model of curriculum integration and reconstruction for teachers in music. This paper provides a new solution for the integration of teaching resources in the field of music education for teachers, which has important theoretical and practical significance.
DynaFuse: Dynamic Fusion for Resource Efficient Multimodal Machine Learning Inference

Multi-modal machine learning (MMML) applications combine results from different modalities in the inference phase to improve prediction accuracy. Existing MMML fusion strategies use static modality weight assignment, based on the intrinsic value of sensor modalities determined during the training phase. However, input data perturbations in practical scenarios affect the intrinsic value of modalities in the inference phase, lowering prediction accuracy, and draining computational and energy resources. In this work, we present DynaFuse, a framework for dynamic and adaptive fusion of MMML inference to set modality weights, considering run-time parameters of input data quality and sensor energy budgets. We determine the insightfulness of modalities by combining design-time intrinsic value with the run-time extrinsic value of different modalities to assign updated modality weights, catering to both accuracy requirements and energy conservation demands. The DynaFuse approach achieves up to 22% gain in prediction accuracy and an average energy savings of 34% on exemplary MMML applications of human activity recognition and stress monitoring in comparison with state-of-the-art static fusion approaches.
Optimization of image processing methods based on wavelet transform and adaptive thresholding

Abstract This paper first classifies the image noise and evaluates the image quality by means of the correlation function, mean square error value, and fidelity. Secondly, an image adaptive threshold denoising system based on wavelet transform is constructed, and the image processing is realized by using the wavelet transform principle and the selection of threshold value. Finally, the image is optimized by using the modulo-maximum denoising method and the threshold denoising method for empirical analysis. The results show that the signal-to-noise ratio of the noisy signal is 6.2315dB, and the signal-to-noise ratio of the modulo-maximum processing is 12.7024 dB. The peak signal-to-noise ratio of the noisy image is 20.1258dB, the peak signal-to-noise ratio of the soft threshold denoising method is 26.4831dB, and the peak signal-to-noise ratio of the hard threshold denoising method is 22.5864dB. This shows that the wavelet transform and adaptive thresholding can effectively denoise and ensure image quality. Image quality.
Intelligent physical education teaching system in colleges and universities based on grey correlation degree model

Abstract This paper first proposes the teaching model of college sports smart classrooms based on an online teaching platform and analyzes its basic structure and teaching process. Secondly, a gray correlation degree model is constructed for the teaching evaluation of the physical education SCT model, and the comparative sequence and reference sequence of the gray system are used to realize the solution of the correlation degree. Finally, the teaching quality evaluation index system of the physical education SCT model was constructed with SD University College of Physical Education, and the effectiveness of the application of physical education SCT model was analyzed. The results showed that the correlation degrees of a teacher teaching, student learning, environmental resources, and curriculum development were 0.317, 0.229, 0.225, and 0.266, respectively, and the physical education SCT model should focus on teaching methods and other aspects of consideration.
Analysis of the problems and countermeasures faced by the innovation and entrepreneurial education of college students in higher education institutions under the background of computer big data

Abstract The issues of teaching innovation and entrepreneurship to college students in higher education institutions are first examined in this research, respectively, in terms of practical activities, evaluation system, and faculty strength. Based on the CIPP theory, the assessment framework for innovation and entrepreneurial education was developed, and the redundant indexes were screened by using the improved hierarchical analysis method. Secondly, the AHM-GRA model was used to conduct an in-depth analysis of the innovation and entrepreneurship curriculum at higher education institutions. Finally, the index system was empirically analyzed through gray correlation analysis. The results show that, on average, the approach utilized in this work performs 12.67% better than the fuzzy comprehensive assessment. With the help of this methodology, higher education institutions may assess their current level of innovation and entrepreneurship instruction and strive to improve it.
A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

Abstract This paper first proposes an integral function algorithm of the support vector machine model for path optimization of physical education and health courses in colleges and universities according to the core literacy integration teaching situation. Secondly, the integral function algorithm is used to map the initial data from the original space to a higher dimensional space so that the samples exhibit linear differentiability in the higher dimensional space and the significance and correlation of the factors influencing physical education and health in colleges and universities are analyzed using spatiotemporal sequences. Then a physical education and health test management system was constructed according to the needs of physical education teachers and students, while an integral function algorithm was used to analyze the behavior of physical education and health knowledge in colleges and universities. The results showed that there was a significant difference (p&gt;0.05) between the knowledge of physical education and health ( X ± SD ) for 10.6 ± 4.5 and physical education and health behavior ( X ± SD ) for 7.2 ± 3.5 showing that there was no significant difference between the knowledge and behavior of physical education and health, which can be considered to have a high degree of consistency in knowledge and behavior.
A Projected Zeroing Neural Network Model for the Motion Generation and Control

Recently, zeroing neural networks plays a vital role in robot control and trajectory tracking. This chapter designs a projected zeroing neural network for redundant robot control, which achieves high superiority and efficiency. The research background about robot control by zeroing neural networks is presented in Sect. 4.1. In Sect. 4.2, we study the feedback-considered scheme of the robot. Neural network design is briefly discussed in Sect. 4.3. The experiments are given in Sect. 4.4. Lastly, the conclusions and future work are concluded in Sect. 4.5.
Correspondence of F.V. Bulgarin and K.F. Kalaydovich

For the first time, the correspondence of the writer, journalist, publisher Faddey Bulgarin with the historian and archeographer Konstantin Kalaidovich is published in full. The article introduces into scientific circulation their letters for 1822–1824, mainly devoted to the materials of the historical St. Petersburg journal “Northern Archive,” the publication of which was undertaken by F.V. Bulgarin. K.F. Kalaidovich, who lived in Moscow, was an experienced employee of the Archive of the Collegium of Foreign Affairs and the chief superintendent of the Commission for the Printing of State Letters and Treaties. He helped the journal with the communication of valuable documents on the history of medieval Rus’ and the Moscow kingdom of the 15th–17th Centuries, having prepared more than 10 publications for the “Northern Archive” in five years. Kalaidovich also acted as an intermediary between Bulgarin and his fellow historians, members of the “circle” of a patron of art, fascinated by national history, Count N.P. Rumyantsev. The publication touches upon the issues of organizing historical research, the controversy around the “History of the Russian State” by N.M. Karamzin, journal policy in Russia in the 1st quarter of the 19th century, and payment for journal publications.
A study of user data privacy protection algorithms in the context of metaverse based on emotional AI IoT

Abstract In the context of the metaverse, user data privacy protection has become an important issue. In this paper, firstly, a user data privacy leakage risk assessment scheme is designed by attribute sensitivity calculation, attribute similarity calculation, and attribute association calculation. Then a data privacy protection algorithm based on differential privacy is proposed, and the differential privacy data protection algorithm and implementation mechanism are described. Finally, the performance of the differential privacy protection algorithm is evaluated by analyzing the learning performance and protection performance of the algorithm. The results show that the learning performance of the differential privacy protection model decreases with increasing τ when q = 0 The larger q is, the better the protection performance of the model is, and the optimal τ value also shows a trend to the right. This study provides an effective method for user data privacy protection under the metaverse and offers new ideas for research in related fields.
Design and development of personalized education information management system based on artificial intelligence

Abstract In order to combine artificial intelligence and personalized education, this paper uses semantic similarity to evaluate the similarity between personalized education models and resource information and gives a semantic similarity calculation between word lists. It is used to measure the semantic similarity between user models and texts and between texts, and a recommendation algorithm based on semantic analysis is constructed. Based on this, the needs of personalized education are analyzed, and the personalized education information management system is designed using the MVC framework with a B/S structure. After system testing, we obtained that the MSE value of the review data volume threshold set to 2 was 0.94 on the student information set, 0.86 on the teacher information set, 0.76 on the course information set, 0.74 on the score information set, and 0.81 on the administrative information set.
Analysis of the innovation path of financial accounting education model from the perspective of integration of industry and education

Abstract By using the CIPP model, evaluation indexes are determined to construct an evaluation system for industry-education integrated financial accounting education. The expert authority coefficient is used to assess the degree of authority level of consulting experts, combined with Kendall’s harmonious coordination coefficient to coordinate data differences. The fuzzy comprehensive evaluation method is used to evaluate the teaching mode of industry-education integration in higher vocational institutions. Combining fuzzy mathematical affiliation, vector and matrix, the fuzzy relationship matrix set of the input of the integration of industry and education in a higher vocational institution is analyzed and the performance of the integration of industry and education is rated. The results show that the overall evaluation value of the integration performance is 76.9433, which is “good” and can be regarded as a good level of integration performance. In the analysis of the practicality of the teaching materials, 35.7% of the students thought that the course knowledge was not practical.
Research on the quality system of psychology courses in higher education institutions based on the background of deep learning

Abstract This paper uses a sentiment analysis approach to study students’ emotional experiences in psychology courses. It mainly analyzes the method of sentiment analysis in education and constructs a model of course quality analysis. The GRU model is used to store the input and output of textual information. The analysis focused on the input module, memory module, recurrent attention module and output module in classroom quality analysis. A third-order confusion matrix was established to calculate the accuracy rate and F1 mean value of common evaluation indexes for effective analysis. The results showed that most teachers allocated classroom content mainly to teaching professional knowledge, so the weights of the first-order indicators were 0.25 for teaching literacy, 0.45 for the teaching process, and 0.15 for curriculum resources.