bluuebunny/crossref_metadata_2025_split huggingface dataset:
«De la novela al cine y a la ficción televisiva: adaptaciones múltiples y nuevas vías de estudio», de Rafael Malpartida y Giovanni Caprara (eds.)
Authors: Alcázar Sainz Daniel 
Published: July 2024

Reseña del libro De la novela al cine y a la ficción televisiva: adaptaciones múltiples y nuevas vías de estudio (Granada: Comares, 2022), de Rafael Malpartida y Giovanni Caprara (eds.).
Con el paso del tiempo, las relaciones entre literatura y cine se han diversificado y hecho más significativas: cada año, más textos son llevados a la pequeña y gran pantalla, algunos de ellos en múltiples ocasiones y en distintos formatos. Con este precedente nace De la novela al cine y a la ficción televisiva: adaptaciones múltiples y nuevas vías de estudio (2022) en el seno de la Universidad de Málaga, mediante un equipo coordinado por Rafael Malpartida y Giovanni Caprara, e integrado además por Míriam López Rodríguez, José Manuel Herrera Moreno y Manuel España Arjona.
«Más perenne que el bronce: el discurso autopoético en la lírica española contemporánea», de José Ángel Baños Saldaña
Authors: Rodríguez Miralles Alberto 
Published: July 2024

Reseña del libro Más perenne que el bronce: el discurso autopoético en la lírica española contemporánea (Santander, Genueve, 2023), de José Ángel Baños Saldaña.
José Ángel Baños, en Más perenne que el bronce (2023), pretende rellenar un vacío teórico en los estudios literarios de España: la sistematización del discurso autopoético en la lírica española y su posterior aplicación a la lírica española contemporánea. En esta reseña, tratamos de poner en valor la obra del crítico por la asentación y ejemplificación de unas bases metodológicas con las que afrontar complejamente lo metapoético, indispensables a la hora de esclarecer la voluntad estética de los autores y la relación de tal voluntad con el contexto literario que les envuelve.
A Mobile English Learning Platform Based on Data Mining and Personalized Recommendations
Authors: Zhao Qiong 
Published: July 2024

Abstract
As mobile technology advances, mobile English learning platforms have increasingly become the predominant mode of language acquisition. However, traditional platforms often suffer from inefficiency due to a lack of targeted and personalized recommendations. This paper explores the enhancement of mobile English learning platforms through the application of customized recommendation algorithms and data mining techniques. We have developed a system architecture composed of a user layer, a business layer, and a data layer, employing Spark and Support Vector Machine (SVM) for parallel data processing. Our approach integrates a collaborative filtering algorithm and a hybrid recommendation model, utilizing user information alongside the dual-tower model Deep Structured Semantic Model (DSSM) and the Deep Interest Network (DIN). Experimental results demonstrate that our recommendation model significantly surpasses traditional models in precision, recall, and F1 scores, with the F1 score improving by approximately 0.1 to 0.4. Furthermore, learners using this platform showed superior outcomes in homework completion, test scores, learning quality, and sustained learning enthusiasm compared to a control group, with an overall improvement of 15.48 points in English proficiency scores. In summary, this study validates the effectiveness of a mobile English learning platform that synergizes data mining with personalized recommendation algorithms to enhance both learning efficiency and user satisfaction.
Leontief Model-Based Research on the Input Structure of Three Industries: A Markowitz Optimization Perspective
Authors: Wang Hong 
Published: July 2024

Abstract
Economic growth necessitates not only the balance between aggregate supply and aggregate demand but also the structural equilibrium among various industries. This paper integrates the input-output table into the Leontief dynamic model, utilizing Markowitz’s theory to construct an investment portfolio of inputs and outputs. Further, it develops an input multiplier model for three distinct industries by amalgamating the Keynesian multiplier theory. It outlines a methodology to compute the influence and inductance coefficients of industrial interrelations. Employing the input-output table from HB province spanning 2010 to 2020, this study examines the input dynamics, investment multipliers, industrial structure, and inter-industry correlations. Over this period, the labor input structure of the primary industry witnessed a decline of 18.36%, while the investment multiplier for total output was augmented by 5.107 units for every unit increase in equipment manufacturing products. Between 2015 and 2020, the influence and inductance coefficients for the three industries rose by 11.21% and 4.45%, respectively, all constrained by the secondary industry. To stabilize the input structure across the three sectors and bolster sustained economic growth, it is imperative to liberate the relevant inputs of the secondary industry and enhance those of the primary and tertiary sectors.
An ELM Algorithm Study of Chinese Wushu Spreading in International Cultural Exchange
Authors: Tan Liang , Wu Jianwei , Chen Qin , Chen Weixiang , Liu Tianyu 
Published: July 2024

Abstract
Strong visual impact and easy-to-understand communication methods have made the YouTube video platform the forefront of international cultural exchange and dissemination of Chinese martial arts. In this paper, we first design a principal component-ridge regression algorithm to correct the multicollinearity problem and eliminate its negative impact on regression analysis. Then, based on the “center path-edge path” perspective in ELM theory, we assumed that the video theme, duration, title, and other factors would affect the dissemination effect of Chinese wushu in international cultural exchanges and constructed an ELM model for the international dissemination of Chinese wushu. Then we designed a questionnaire with international students in China as the research object, conducted sample reliability and validity tests, and combined the principal component-ridge regression algorithm to explore the influencing factors of Chinese Wushu in international cultural exchange communication. The results are presented as follows: except for the p-value of 0.021 and 0.004 for video scientificity and the presence or absence of characters on the video cover, the p-value of all the other items is less than 0.001. The video theme, video scientificity, video duration, video title, cover characters, cover text, and animated video all have a significant positive impact on the effect of dissemination of Chinese Wushu in international cultural exchanges. The theoretical basis of this paper is used to accelerate the construction of Chinese Wushu cultural influence and optimize the communication strategy of Chinese Wushu international cultural exchange.
EEGVision: Reconstructing vision from human brain signals
Authors: Guo Huangtao 
Published: July 2024

AbstractThe intricate mechanisms elucidating the interplay between human visual perceptions and cognitive processes remain elusive. Exploring and reconstructing visual stimuli from cerebral signals could help us better understand the processes by which the human brain generates visual imagery. However, the inherent complexity and significant noise in brain signals limit current efforts to reconstruct visual stimuli, resulting in low-granularity images that miss details. To address these challenges, this paper proposes EEGVision, a comprehensive framework for generating high-quality images directly from brain signals. Leveraging the recent strides in multi-modal models within the realm of deep learning, it is now feasible to bridge the gap between EEG data and visual representation. This process starts with a time-frequency fusion encoder in EEGVision, which quickly pulls out cross-domain and robust features from EEG signals. We then design two parallel pipelines to align EEG embeddings with image features at both perceptual and semantic levels. The process uses a stable diffusion-trained image-to-image pipeline that combines coarse and fine-grained data to get high-quality images back from EEG data. Both quantitative and qualitative assessments affirm that EEGVision surpasses contemporary benchmarks. This network architecture holds promise for further applications in the domain of neuroscience, aiming to unravel the genesis of human visual perception mechanisms. All code is accessible viahttps://github.com/AvancierGuo/EEGVision.
Path Updating of the Legal Ontology Model for Personal Information Protection in the Age of Big Data
Authors: Hu Wenyu 
Published: July 2024

Abstract
This paper proposes a legal ontology model for personal information protection to extract structured information on the high-frequency elements of common privacy leakage and data security cases as key information of judicial documents. The prediction model of relevant sentences based on the CART decision tree is used to predict the outcome of the defendant’s sentence. The application scenarios of the legal ontology model of personal information protection in judicial guarantee are explored to guarantee the fairness of adjudication of personal information protection cases in terms of reliability of legal counseling, accuracy of sentencing assistance, and categorization of cases. The results show that 113 sample cases were retrieved by keyword search, excluding duplicate cases, with Beijing having the highest number of 42. With the development of the big data era, there has been a significant increase in personal information protection cases after 2019. The legal ontology model constructed in this paper has only 56 instances of classification error, the Macro-F1-Score and Micro-F1-Score indicator values are above 70%, and the utility values of personal information protection-related cases are all higher than 0.5. Therefore, the legal ontology model for personal information protection constructed in this paper is accurate in classification and sentencing prediction, with high reliability, and realizes the path of the big data era.
Legal Risk Prevention for Personalized Recommendation Algorithms
Authors: Hu Wenyu 
Published: July 2024

Abstract
Personalized recommendation algorithms, designed to cater to users’ individualized preferences and enhance the efficiency of Internet information services, have inadvertently precipitated significant legal risks, notably the potential for user privacy breaches. This development necessitates prompt and appropriate legal responses. This paper presents an approach utilizing cosine similarity and Pearson correlation to establish a set of nearest neighbors, facilitating the identification and querying of the closest users based on similarity metrics. Subsequently, we implement a collaborative filtering recommendation algorithm tailored to these personalized settings. We thoroughly evaluate the legal risks associated with the application of customized recommendation algorithms, supplemented by detailed case analyses. The Pareto chart of risk crisis factors reveals that the frequency of issues related to policy legislation and technical concerns are 165 and 95, respectively, cumulatively accounting for 46.68% of the probability share—nearly half of all identified factors. This highlights the critical need for focused attention in these areas. Furthermore, our analysis calculates the probability and severity of data leakage across various paths within the recommendation process. Results indicate severities of 0.029, 0.0333, 0.0409, 0.0447, 0.0481, and 0.165, with the most significant vulnerability appearing in the final recommendation phase to the user, underscoring the imperative to prioritize data privacy in this module. In response, we propose modifications to the existing models to incorporate principles of differential privacy, aiming to mitigate these legal risks and enhance the security and reliability of personalized recommendation algorithms. This initiative is essential for aligning technological advancements with stringent privacy standards and legal requirements.
Research on the Root Cause Analysis and Governance Measures of Frequent Power Outages in Electric Power Enterprises
Authors: Wang Zhixin , Wang Chong , Zhang Hui , Wang Jingzhi , Tong Cuizhi 
Published: July 2024

Abstract
Accompanied by the rapid development of the economy and the great strides of reform, the whole society puts forward higher and higher requirements for the safety and stability of the power grid, and the frequent power outage events will bring great losses to the power enterprises. In this paper, we employ the linear encoding function in the MAGNN method to linearly process the risk factors associated with frequent power outages in electric power enterprises. We then assign weights to these linearly processed risk factor vectors using a graph attention mechanism. Finally, we apply the correction matrix of outage risk factors to identify the risk of power outages in electric power enterprises and design an intelligent power outage control system. The study discovered that 10 kV system failure is the primary cause of frequent outages in Power Enterprise-A. The number of failures occurred 129 times due to 10kV feeder system failures in rural distribution networks. The study also reveals that the implementation of an intelligent outage control system resulted in a 73.60% year-over-year decrease in the reliability loss of fault outages in power enterprise A, thereby effectively addressing the issue of frequent outages and reducing the economic losses incurred by power enterprises. This paper provides an effective method for resolving frequent power outages in electric power enterprises and provides a guiding direction for improving power supply reliability in electric power enterprises.
Research on the Integration and Development of Visual Professional Education and Book Creative Design Combined with Association Rule Mining
Authors: Xie Xi , Xia Yuqing 
Published: July 2024

Abstract
The visual professional education of existing colleges and universities can no longer meet the needs of the development of the times. In view of such a situation, this paper explores the integration and development of visual professional education and book creative design using association rule mining. Based on xAPI technology, a data collection platform has been designed to provide security, easy scalability, and real-time data acquisition for visual professional education and creative design text data acquisition. Afterward, the data is preprocessed so that it can be used for association rule mining in the future. The support degree, confidence degree, and enhancement degree of the two are defined to achieve the purpose of constructing the Apriori association rule mining analysis model for the fusion development of visual professional education and book and cultural and creative design. The analysis of the association rules of visual education and creative design shows that 10 association rules are obtained when the minimum support degree is 0.4. The minimum confidence degree is 0.6, and the confidence degree of the practical activities in visual professional education for the cultural elements and creative elements in book and creative design is 0.657, 0.619. The degree of enhancement is 0.709 and 1.021, and the correlation rules of all professional education and book and creative design show a synchronous enhancement of the correlation rules. Association rules show a synchronized enhancement of correlation.
Machine Learning Based Analysis and Prediction of Emotional Expressions in Dance Movements
Authors: Huang Meigui 
Published: July 2024

Abstract
Dance movement is a powerful way to convey human emotions, and analyzing and predicting the emotional expression of dance movement through machine learning has become a hotspot in the field of artificial intelligence research nowadays. This paper employs the Seagull optimization algorithm to enhance the SVM classification model, laying the algorithmic groundwork for the research, and refines it to align with the research requirements. The classification of dance movements is accomplished by the nonlinear regression algorithm in the support vector machine regression algorithm, while the task of capturing dance movements is realized by means of Euler angles to describe the orientation, rotation matrices to transform vectors between different coordinate systems, and quaternions to optimize the Euler angles. Finally, this paper analyses and predicts the emotional expression of dance movements using a classification loss model (LSTMBO) and a W-RNN model that incorporates the weights of emotion words. In this paper, tests on the classification algorithm revealed that the algorithm's classification accuracy is above 90% for all datasets used in the research. Moreover, the performance and effectiveness of dance action capture are significantly better than other comparative algorithms. Simultaneously, this paper's algorithm achieves an accuracy of over 80% in predicting emotions expressed through dance movements. Numerous experiments have proven the effectiveness and superiority of the algorithm model in this paper, thereby promoting the research and development of the field.
Augmented Reality-based Interactive Typography Experience and Evaluation
Authors: Li Hong 
Published: July 2024

Abstract
This paper explores the innovative integration of Augmented Reality (AR) technology into font design, emphasizing the user experience, visual performance, and spatial experientiality of AR interactive fonts. Central to this investigation is the deployment of AR-based calibration technology, which establishes a spatial coordinate system specifically for AR fonts. When synchronized with the camera model, these virtual fonts are seamlessly registered within real-world scenes. To enhance user engagement, gesture interaction design is incorporated, enabling interactive functionalities within the font system. The effectiveness of this design is evaluated through user testing, where the interactive performance is assessed based on audience experience post-implementation. The findings reveal that interactive fonts are highly effective in content display, achieving a user acceptance rate of 0.7659, and in content retrieval systems, with a rate of 0.7404. Audiences demonstrated a preference for conducting operations independently and showed a particular inclination towards font performance-based projects. The study concludes that the interactive font design project, which leverages augmented reality and gesture interaction technologies, holds substantial commercial potential.
Virtual Reality Technology Applied to the Immersive Experience of Teaching Art History
Authors: Pei Xiaopeng 
Published: July 2024

Abstract
Based on the demand to adapt to the future development of education, the application of virtual reality technology in the field of education is becoming more and more extensive and in-depth. This paper applies virtual reality technology to art history teaching, constructs an art history teaching method using virtual reality technology, and designs a complete teaching process. The EEG signals of students in the teaching process are collected and pre-processed. The features in the EEG signals are extracted by using the SPCNN model with dual convolutional kernels in parallel. The EEG features are output through the convolutional layer and the all-connected hierarchy processing. A support vector machine calculated the maximum distance between the samples and the hyperplane, and the classification and recognition results of EEG features were obtained. The frontal channel TBR change values of each subject student in virtual reality technology art history teaching were significantly lower than those of the traditional art history teaching mode (p<0.01). The frontal F7 channel TBR values of the subject students in different teaching modes were significantly different (p=0.004<0.01). Meanwhile, students gained a strong sense of presence in virtual technology teaching, and the motivation of students to learn art history after participating in virtual reality technology teaching was significantly higher than that of the pre-test. This paper’s teaching model is designed to promote students’ immersion and concentration in art history teaching.
Consumer evaluation mechanisms on e-commerce platforms: reputation management and analysis of influencing factors
Authors: Wang Lidong 
Published: July 2024

Abstract
This paper proposes a regression algorithm to statistically relate the relationship between the relationship variables and proposes a multiple linear regression model to conduct a linear correlation study between a dependent variable and multiple independent variables. The factors that affect the value of merchant reputation in the content of consumer evaluation are regressed and analyzed, and a model for evaluating merchant reputation is proposed based on the regression results. In the merchant reputation evaluation model, the Bayesian algorithm and multi-source data fusion method are used to calculate the merchant weight, the Euclidean distance is used to calculate the platform weight, and the judgment matrix is constructed to calculate the merchant e-commerce reputation score. In the model performance test, 10 Taobao merchants are randomly selected for reputation calculation. The highest merchant reputation scores of 4.582 and 4.584 can be obtained for merchants S9 and S10, and the index scores are more accurate. The original Taobao reputation scores of merchants S4, S8, and S9 are all 4.97. At the same time, the model in this paper calculates them to be 4.404, 4.304, and 4.582, respectively, which are more reflective of the real reputation level of merchants.
Practices and Challenges of Artificial Intelligence-Assisted Teaching in Vocational Undergraduate Public English Classrooms
Authors: Wang Yanhua , Cui Cui 
Published: July 2024

Abstract
This paper first introduces the architecture of the natural language processing system and then analyzes how natural language processing decomposes the probability distribution function by multi-factor form and Bayesian formula to improve the efficiency of function description. Then, NLM is applied to enhance the statistical coefficients of the N-gram model, which solves the problems of data sparsity and dimensionality disaster. By using natural language understanding in English teaching practice and experimenting with the teaching effect, it was found that the overall English achievement of the experimental group increased by 5.4 points, the average Z-score reached more than 3 points, and 96.6% of the students were interested in the teaching method. The new teaching method is demonstrated to have a significant impact on the improvement of English scores.