bluuebunny/crossref_metadata_2025_split huggingface dataset:
EKSISTENSI HAK NON KEBENDAAN DALAM UNDANG-UNDANG NOMOR 1 TAHUN 1974 TENTANG PERNIKAHAN DAN HUKUM ISLAM
Authors: Arsan A.Varadiba Maywardana , Sari Muspita 
Published: August 2024

The existence of non-material rights exists and is regulated in Islamic Law and Law no. 1 of 1974 concerning Marriage. In Islamic Law, non-material rights are regulated, for example, the wife's non-material rights, the husband is obliged to educate his wife to be religious, the husband has the best relationship with his wife and the husband is obliged to look after his wife. Then the husband's non-material rights, the wife is obliged to look after herself and her husband's property, not to make a sour face in front of her husband and not to do anything that her husband doesn't like. Furthermore, in Law no. 1 of 1974 non-material rights are regulated in Articles 30 to Article 34. According to Islamic Law, these non-material rights or rights and obligations are terminated if the wife applies nusyuz to her husband as well as the husband who applies nusyuz to his wife. Apart from that, a husband's obligations are terminated if he acts cruelly towards his wife. Meanwhile in Law no. 1 of 1974 non-material rights or rights and obligations. If the husband and wife are negligent in their rights and obligations, each can file a lawsuit with the Court as in Article 34 paragraph (3).

Keywords: Existence, Non-Intangible Rights, Islamic Law
Research on Educational Guidance of College Students’ Career Planning Awareness in the Context of Informatization
Authors: Xiong Binru 
Published: August 2024

Abstract
The rapid development of science and technology has brought society into the era of digital intelligence, which not only changes people’s way of production and life but also brings unprecedented changes to practical courses such as career planning for college students. The study constructs a model of factors influencing college students’ career planning based on relevant theories of college students’ career planning education. After the correlation analysis and regression analysis of the career planning ability and the factors, as well as the difference analysis of the career planning ability of college students, the model of career planning education for college students based on SWOT analysis is proposed. There are significant correlations between family care support, career lectures, mentoring behavior, career information acquisition, and career planning ability (p-value is less than 0.005). The four independent variables have an impact coefficient of 0.186, 0.010, 0.092, and 0.291 on the career planning abilities of college students. P-value of significance of gender, major, age, and career counseling experience on each dimension of college students’ career planning is less than 0.05, and there is a significant difference between college students’ career planning ability in gender, major, age, and career counseling experience.
Informatization Development Strategy for Rural Financial Services
Authors: Liu Xiaowan 
Published: August 2024

Abstract
Promoting the development of rural financial informatization is an important magic weapon to help rural revitalization, and it is also a necessary way to comply with the trends of the times. This paper constructs a quantitative model of farmers’ investment behavior based on the VaR method and the Morning Star performance evaluation method to provide financial service institutions with ideas for financial product innovation in order to solve the current problems of the increasing level of rural financial service informatization but the uneven degree of informatization and the weak sense of innovation in the construction of rural financial service informatization. In order to make full use of rural financial service informatization resources and reduce the investment cost and time of farmers, this paper designs and implements the financial product recommendation algorithm based on the investment behavior of farmers using the AE_xDeepFFM recommendation algorithm. The analysis finds that farmers’ investment behavior is more significantly affected by economic and social value perception and social influence, and the path coefficients of the two on behavioral intention in the resultant model are 0.411 and 0.407 at the significant level, respectively. Effort expectation, on the other hand, indirectly affects the investment intention and behavior of farmers through performance expectation.
Deep Learning-Driven Real-Time Facial Expression Tracking and Analysis in Virtual Reality
Authors: Liu Yinuo 
Published: August 2024

Abstract
In this paper, we use VR equipment to collect relevant facial expression images and normalize the angle, scale, and gray scale of the collected images. The direction quantization of image features is realized by 3D gradient computation, and then the histogram of the direction gradient of each video sub-block is cascaded into the final HOG3D descriptor so as to complete the extraction of dynamic expression features. In view of the multi-dimensional problem of the features, it is proposed to use a principal component analysis algorithm to reduce their dimensionality and use a multi-layer perceptron and deep confidence network to jointly construct the facial expression tracking recognition model. The datasets are used to analyze real-time facial expression tracking in virtual reality. The results present that the verification correctness of both datasets A and B reaches the maximum at the 120th iteration. In contrast, the loss value reaches the equilibrium state quickly at the 40th iteration. The dynamic occlusion expression recognition rate of the deep confidence network on dataset A (66.52%) is higher than that of the CNN (62.74%), which fully demonstrates that the method of this paper is able to effectively improve the performance of real-time facial expression tracking performance in virtual reality. This study can help computers further understand human emotions through facial expressions, which is of great significance to the development of the human-computer interaction field.
Application and Path Exploration of PDCA Management Model in Higher Vocational Education Management
Authors: He Wenwen 
Published: August 2024

Abstract
With the rapid development of society, the total education demand for higher vocational colleges and universities is growing at an extremely fast rate, and the original education management mode can no longer meet the needs of higher vocational education in the new era. The PDCA management model is a continuous improvement model that can achieve the objective of continuous improvement processes and perfectly fits the needs of higher vocational education management. This paper introduces the support vector machine, an algorithm suitable for high-dimensional classification problems, into the PDCA management model to carry out a multidimensional analysis of vocational high school students’ behavioral data, optimizing the checking part of the PDCA management model so that the cycle can continue to play an ideal role. The results of the application of the prediction of academically abnormal students show that the total number of academically normal students is 12,823, the total number of academically abnormal students is 2,981, and the proportion of abnormal students in different schools can be as high as 34.82%, and as low as 12%. The SVM algorithm, in the case of the imbalance between the proportion of academically abnormal and academically normal students, the model is still able to find out the majority of the students with abnormal studies, and it plays a central role in the PDCA management model to make the management of higher vocational education more perfect. By playing a central role, it enhances the management of higher education and creates a unique management path.
Construction and Application of Deep Learning Models for Evaluating the Effectiveness of International Communication of Chinese Music
Authors: Wang Mingjie , Qi Xuejing 
Published: August 2024

Abstract
In recent years, research on deep learning has become an important topic in artificial intelligence, has achieved excellent results in several evaluation fields, and has become a cutting-edge method for solving various evaluation problems. For this reason, this paper explores the effects of China’s international music communication from three dimensions: cognition, attitude, and behavior, and establishes an evaluation system containing 11 secondary indicators. Then, an assessment model of the music communication effect is constructed using the deep learning network method. The constructed assessment model was used to evaluate the impact of international communication on Chinese music. According to the output of the model, it was determined that the communication power of the selected 1,120 Chinese music songs belonged to the solid and average levels, accounting for 71.34% of the total, and the comprehensive evaluation score was between 2.5 and 4.3. This indicates that Chinese music has achieved better dissemination effectiveness and influence with the help of various music platforms.
A Practical Approach of Integrating Traditional Cultural Elements into Art and Design Talent Cultivation in Colleges and Universities under the Background of Deep Learning
Authors: Jiang Meng 
Published: August 2024

Abstract
Chinese traditional culture has rich cultural connotations, which has an important role in promoting the development of modern art and design education. The study examines the application of deep learning technology in art and design education through the analysis of several target design colleges and universities based on the background of deep learning. Then, it starts from the creation of art images based on traditional cultural style migration, constructs a style migration model based on a convolutional neural network, and evaluates the effect of style migration images of traditional cultural elements. Create teaching experiments to investigate the utilization of deep learning technology in art and design education. At present, teachers and students still need to improve their cognition of the use of deep learning technology in art design, and the use of deep learning technology in art design has been popularized to a certain extent, with computer vision technology (90.7%) and natural language understanding technology (71.2%) being the most used. The images generated by the style migration model integrating traditional cultural elements have high PSNR values (12~20) and SSIM values (0.375~0.633), and the performance of the students using the model is 6.98% higher than that of the control students, which reflects the validity and feasibility of the application of the model in the creation of art design. It is necessary to optimize the curriculum system, enrich the teaching mode, cultivate the appreciation ability, carry out practical activities and create a good atmosphere and other dimensions to promote the talent cultivation of traditional culture in art design.
Dual-line Cultivation of Students’ Professional Skills and Creative Thinking in Vocational Education in the Context of Emerging Technologies
Authors: Li Ke , Zhang Li 
Published: August 2024

Abstract
In order to improve the teaching quality of vocational education, explore the vocational education model that meets the market demand, and build a distinctive vocational education system, it plays an important role in analyzing the research trend of vocational education. The process of text mining is first described in this paper, which primarily involves text data acquisition, text preprocessing, latent information extraction, and data statistical analysis. Then, in view of the shortcomings of the traditional K-means clustering algorithm, the algorithm is improved on this basis, and DT-Kmeans clustering is used to study and verify the effectiveness of the algorithm. The training tendency of each vocational education school was investigated. The keyword feature extraction was completed, and the integrated education model of each vocational education school was relatively unified. Among the top five keywords, each school had a high bias towards “natural science” and “ideological and political education”. Eighteen schools (90%) believe that “ideological and political education” occupies a “very important” position in student training. Two schools (10 percent) considered it “relatively important”.
Application of Artificial Intelligence Technology in Smart Building Integrated Management Platform under Big Data Environment
Authors: Wu Zheng 
Published: August 2024

Abstract
The rapid development of the construction industry has led to a wider application of artificial intelligence technology, and the application of this technology to the comprehensive management of intelligent buildings can promote the construction of intelligent construction sites and improve the level of quality management. In this paper, on the basis of elaborating on the intelligent building design, the framework of the intelligent building comprehensive management platform is built by combining 5D-BIM technology and artificial intelligence technology, and engineering cost modeling is carried out based on the whole process control of engineering cost. The engineering cost prediction model is established based on the BP neural network, and a genetic algorithm is introduced to optimize the initial threshold of the model so as to obtain more accurate prediction results. To verify the effectiveness of the intelligent building integrated management platform application, data analysis is carried out across three dimensions: engineering cost control, prediction, and application. The whole process of cost control makes the peak value of cost-effectiveness reach 4.25*108 yuan, the average value of project cost reduction reaches 78.68%, and the relative error of project cost prediction results minimizes to only 1.77%. The process of processing project information was shortened and improved by 9.15% and 8.06%, respectively, and the backlog reduction rate of project materials could be reached by 24.76%. The intelligent building integrated management platform that utilizes artificial intelligence technology promotes construction project intelligence and effectively reduces construction costs.
Research on Optimization Strategies of Student Financial Aid System in Colleges and Universities Based on Multi-Objective Decision-Making Model
Authors: Zhou Jing 
Published: August 2024

Abstract
The identification of economically disadvantaged students in colleges and universities is the primary link in the college and university financial aid system, and it is the prerequisite, foundation, and guarantee for the implementation of various national financial aid policies and measures. This paper takes the Q college student financial aid system as the research object and discusses the authenticity of the information, the standard qualitative and quantitative difficulty in the determination of the work of standardized operation, and other problems in the determination of the work in depth. By analyzing the operation level of the student financial aid system of the university and according to the existing problems of the current financial aid system, a multi-objective decision-making model is designed and optimized, and the qualitative evaluation in the fuzzy multi-objective decision-making method is used to consider multiple factors in the determination process, so as to ensure the accuracy and fairness of the financial aid. This paper proposes an optimization strategy for the student financial aid system in colleges and universities based on the model’s identification results. A questionnaire survey is re-conducted on Q colleges and universities that use the optimized student financial aid system. The results show that the optimized student financial aid system is more helpful for students’ academic lives.
A Study of Reinforcement Learning Algorithms for Artistic Creation Guidance in Advertising Design in Virtual Reality Environments
Authors: Zhu Leyi 
Published: August 2024

Abstract
The innovative application of virtual reality technology in advertising design greatly promotes the development of advertising design work in the media field and also provides a new guiding direction for art creation in advertising design. After analyzing the advantages and performance of VR technology applied in advertising design, the article elaborates on the use of art creation rules and related skills in advertising design. To facilitate the guidance of art creation in advertising design within a virtual reality environment, this paper employs chunked feature-matching technology to visually process print advertisement images, thereby providing an interactive design process for advertisements in virtual reality. Then, using a reinforcement learning algorithm as a framework, we combine the DDPG strategy with a neural renderer to construct a virtual reality advertisement design art creation style migration model and conduct design experiments to verify its effectiveness. Finally, we conduct an experience evaluation analysis for the design of virtual reality advertisements. The model in this paper converges after 225 epochs, at which time the loss value is 17.09% lower than that of the CycleGAN model; the mean value of the advertisement image style migration score is 4.08; and the mean value of the comprehensive score of the on-campus test for virtual reality advertisement design is 8.12. Reinforcement learning not only facilitates the art creation guidance of advertisement design in a virtual reality environment but also enhances the user’s understanding of the advertisement design content, thereby laying the foundation for improving the communication effect of virtual advertisements.
Optimizing Collaborative Filtering Recommendation Algorithms for Knowledge Sharing in Libraries
Authors: Ji Ying 
Published: August 2024

Abstract
With the rapid development of information technology, how to enable teachers and students to quickly find and filter information of interest in massive collections has become a hot issue for many scholars. This paper enhances the traditional collaborative filtering algorithm by utilizing the knowledge-sharing model. Specifically, we calculate similarity using keyword information from items and dynamic information from users based on similarity calculations related to item features and user attributes. The relevant information about users and items is fully utilized to successfully alleviate the problem of new items and new users, and the entire process of collaborative filtering recommendations for libraries is designed. The improved collaborative filtering-based algorithm can achieve a recommendation accuracy of more than 60% and recommend more accurate books to users. The average recommendation rate of the book recommendation algorithm is 0.056 higher than the average recommendation rate of other recommender systems, indicating a higher recommendation rate that can better match the needs of users and alleviate the cold-start problem to a certain extent.
Optimization and Upgrading of Big Data Processing Techniques in High Performance Computing Environments
Authors: Li Jianguang 
Published: August 2024

Abstract
Currently, high-performance computing environments are facing challenges such as limited resources and an increasing number of users. In order to improve the utilization of environmental resources, this paper proposes a high-performance hybrid computing architecture based on big data processing technology, which is constructed on the basis of an HDFS distributed system combined with MapReduce framework and GPU virtualization technology. The PageRank algorithm is utilized to evaluate the performance of rack nodes in the high-performance computing environment, and the evaluation results are applied to design an improvement strategy for task allocation and scheduling through the MapReduce framework. A division function is introduced to dynamically divide the Reduce data, and an approximate sampling method based on sampling information is proposed to guide the setting of the number of Reduce. The IB algorithm is used to cluster the labeled files, and a rack-aware strategy is designed based on HDFS to achieve resource load balancing. The MapReduce-based task allocation scheduling scheme has a reduction in job execution time of up to 39.83% compared to delayed scheduling. The dynamic partitioning design can achieve data load balancing by partitioning 5.382% of the groups and migrating 1.207% of the KVs if the data skew is 1.0. Dynamic balancing of environmental resources and resource scheduling optimization in high-performance computing environments can be achieved through the use of big data processing techniques.
Research on the Innovation Path of Teaching Methods of Civics Classes in Colleges and Universities Based on K-means Cluster Analysis
Authors: Huang Aiwa , Li Ni , Chen Jinyan 
Published: August 2024

Abstract
The innovative design of teaching methods of Civics classes in colleges and universities is an effective measure to help the teaching of Civics classes in colleges and universities. In this paper, the significance of innovative Civics teaching methods is addressed, and a novel path for teaching Civics courses in colleges and universities is proposed. Through feature selection, the K-Means clustering algorithm is optimized, and after optimal selection of the initial clustering center, balanced discriminant function, and combing of the optimization process, the model is applied to the actual innovation of Civics and Political Science teaching, quantitatively assessing the behavioral characteristics of the students and constructing the prediction model to achieve dynamic monitoring of students’ performance. The empirical teaching analysis method is aimed at quantitatively assessing the path proposed in this paper. The clustering results show that consistent with the preset, the model divides students’ learning behavioral characteristics into four categories with clear boundaries and good clustering effect, and most of the student’s grades are concentrated around 60-80 points, according to which the teachers can adjust their teaching strategies in time to improve the teaching effect. Through the control experiment, we know that for the students who have gone through the innovative Civics teaching mode, the mean value of Civics achievement is 71.594, which is 19.329 points higher than the students who have studied in traditional Civics teaching, which is confirmed by the P-value in the t-test of independence which is less than 0.05. It indicates that the innovative teaching method for civics has a significant effect on improving students’ civics achievement.
Research on Feature Extraction Strategies for Cybercrime Crimes Combined with Deep Learning and Their Probabilistic Models
Authors: Lei Yang , Liao Lingyu 
Published: August 2024

AbstractIn this paper, in order to improve the accuracy and precision of the detection and identification of cybercrime cases, eliminating the incomplete and ambiguous information obtained from a single source of evidence, combined with the procedural steps of the sampling and forensics for the detection of cybercrime cases, a cybercrime detection and control model based on the fuzzy reasoning and the improvement of the D-S algorithm is proposed. Combined with the application of the improved D-S evidence theory in the fusion of cyber evidence, the weighted Bayes is combined to achieve effective forensics of cybercrime evidence. The improved D-S evidence theory algorithm achieves a detection rate of 0.989 for network anomalous data, which has an obvious advantage in network data fusion over the no-preprocessing, original D-S evidence fusion algorithm. The intrusion path generation algorithm considers the intrusion risk of intrusion path IV5 to be the highest at 0.186 with the intrusion paths <H1, W2, S2, S3, W5, S3, T5>.