This paper presents a case study of developing a self-supervised keyword extraction system for Wanfang Data, a leading Chinese academic database platform facing the challenge of tagging millions of short-article titles without access to manually labeled data. The study proposed an attentive autoencoder framework that integrated a multihead self-attention mechanism for contextual representation, an importance scoring network for keyword ranking, and a dual-loss optimization function combining reconstruction loss and sparsity loss to enable accurate keyword identification without manual annotation. Using 7,278 real article titles from Wanfang's production environment as the test corpus, the study evaluated the system against five baseline methods. The results showed that the proposed approach achieved an F1-score of 0.3581 and a mean average precision of 0.4544 for top-three keyword extraction, outperforming all baselines and demonstrating 15–20% improvement in ranking accuracy on very short texts (tokens < 10).
This paper constructs a decision-support framework comprising data, rules, interventions, and feedback for ecological education, addressing the demand to integrate healthy consumption upgrading and ecological education. Based on students' physical measurement data over the past three years, this paper introduces transaction compression, hash pruning, and information entropy approximation to improve the Apriori algorithm, mines correlations among physical fitness indicators, and analyzes the current state of teaching practice using teachers' questionnaires. The results show that the improved algorithm can significantly enhance operational efficiency while maintaining rule consistency and effectively identify stable correlations between speed and strength and other key physical fitness indicators. The research shows that the data-driven method can provide a basis for operable teaching interventions and resource allocation for the ecological reform of physical education classrooms, and offer a reproducible case path for implementing ecological education in college and university physical education courses.
This study investigates the determinants of translators' willingness to adopt crowdsourcing translation platforms through a sequential explanatory mixed-methods design grounded in pragmatism. In Phase 1, a stratified random sample of 300+ practicing translators completed an online questionnaire derived from the Unified Theory of Acceptance and Use of Technology 2 and augmented with trust and risk constructs. Partial least squares structural equation modelling revealed that performance expectancy, effort expectancy, community support, and trust significantly predict behavioral intention, whereas perceived risk exerts a negative moderating effect. In Phase 2, semi-structured interviews with 15 purposively selected survey respondents unpacked these statistical relationships, showing how cultural values, volunteer fatigue, and platform recognition shape sustained engagement and offering actionable guidance for platform designers, agencies, and policymakers seeking to enhance the sustainability and inclusivity of crowdsourced translation ecosystems.
With the convergence of AI and learning analytics, business English training is shifting from a content-driven to a learner-driven approach. This study designed an adaptive learning framework that enhanced workplace communication skills using large-scale behavioral data, emotional corpora, and professional scenarios. The system employed multimodal algorithms to create real-time learner profiles and a reinforcement learning scheduler to adjust task difficulty and feedback. A knowledge graph-powered resource engine provided on-demand microlearning and optimized content via closed-loop assessment. The framework integrated linguistic accuracy, communicative effectiveness, and professional relevance, surpassing traditional one-way platforms. Findings showed that multichannel data enriched system perception and hierarchical strategies adapted flexibly to tasks. Scenario-based evaluation enabled contextual progress tracking. This research offered cost-effective corporate training strategies with implications for global remote collaboration and digital workplaces.
Cloud computing has become a key driver in higher education reform, especially for college English online course platforms. Traditional platforms face challenges such as uneven resource distribution, limited computing power, and high operational costs. Cloud computing, with its flexible scalability, resource sharing, and high concurrency processing capabilities, offers a new solution for the integration, distribution, and dynamic scheduling of English teaching resources. This paper explores the application of cloud computing in college English online course platforms, proposes an optimized model for system operation and resource allocation, and verifies its effectiveness through case studies and experiments. The results show that cloud computing enhances the platform's scalability and user experience and that it supports the modernization and intelligent development of college English education. Future work will focus on enhancing personalized services, protecting data privacy, and innovating operational models for sustainable development.
This study investigates how linguistic framing influences the strategic behaviour of large language models in repeated interactions. Four models (Mistral, Qwen3, Llama3.2, Llama3.3) were embedded as autonomous agents in a simulation of a 25-round iterated prisoner's dilemma under three prompt conditions: neutral, positively biased, and hunger-framed. Although payoff structures remained constant, linguistic variation produced substantial behavioural divergence. A one-way analysis of variance showed significant prompt effects in 13 out of 16 model pairings (adjusted p < 0.05). Positively biased prompts increased cooperation by 4–9 percentage points, while survival-framed prompts increased cooperation up to 80 percentage points. While Qwen3 and Llama3.3 were highly sensitive to framing, Llama3.2 showed minimal responsiveness. Several models exhibited emergent strategies such as conditional cooperation and end-game defection. These findings indicate that subtle linguistic cues can systematically modulate cooperative behaviour in large language model agents.
This case study investigates how core strength and aerobic endurance influence suspect control efficiency in police operations through the mediating role of neuromuscular coordination. Using digital motion capture, surface electromyography (sEMG), and blockchain-based data management, the study quantifies physical performance and ensures forensic data integrity. A sample of 150 police officers and cadets participated in standardized confrontation simulations. The results show that neuromuscular coordination significantly mediates the relationship between physical fitness and control efficiency, improving model explanatory power by 50%. These findings highlight the importance of integrating physiological data analysis with information technologies to enhance law enforcement performance, reduce operational risks, and strengthen the reliability of digital forensic evidence.
This paper explores a neural network system designed to optimize film visual effects in mid-sized studios. Using a deep learning approach with convolutional neural networks and attention mechanisms, the system addresses inefficiencies in manual workflows, such as inconsistent actor-computer-generated image interactions and excessive lighting rework. The case study focuses on the 2024 fantasy short Mountain of Mist, produced by Nanjing's Cloud Frame VFX Studio, with a & YEN;500,000 budget. The system utilizes cloud deployment on Amazon Web Services p3.8xlarge instances and human-artificial intelligence collaboration in which artificial intelligence generates base frames and artists add creative refinements. For key visual effects elements like dynamic "ghost mist" and bioluminescent undergrowth, the system reduced production time by 85% and lighting rework by 83.3%, while achieving theatrical-grade quality (peak signal-to-noise ratio of 34.5 db; structural similarity index of 0.94). The findings demonstrate the system's potential to enhance precision, reduce costs, and improve scalability for mid-sized studios.
This study investigates the determinants of translators' willingness to adopt crowdsourcing translation platforms through a sequential explanatory mixed-methods design grounded in pragmatism. In Phase 1, a stratified random sample of 300+ practicing translators completed an online questionnaire derived from the Unified Theory of Acceptance and Use of Technology 2 and augmented with trust and risk constructs. Partial least squares structural equation modelling revealed that performance expectancy, effort expectancy, community support, and trust significantly predict behavioral intention, whereas perceived risk exerts a negative moderating effect. In Phase 2, semi-structured interviews with 15 purposively selected survey respondents unpacked these statistical relationships, showing how cultural values, volunteer fatigue, and platform recognition shape sustained engagement and offering actionable guidance for platform designers, agencies, and policymakers seeking to enhance the sustainability and inclusivity of crowdsourced translation ecosystems.
In this era of rapid modernization, traditional music and dance risk losing both cultural depth and relevance to audiences. Multimedia technology and computer-aided design are integrated into traditional art forms to preserve semantic integrity and architectural coherence. They used a cultural information system framework to prioritize geometric precision and cultural meaning, preventing symbolic shallowness. The hierarchical architecture, semantic modeling, and interaction layers ensure standardized real-time data synchronization. Case studies of stage performances confirmed enhanced aesthetics and accurate cultural translation. This integration enables dynamic revitalization, technical intervention, and heritage preservation, thereby offering a replicable model for sustaining traditional arts through the systematic application of information technology.
The authors propose an abstract meaning representation (AMR) sequence-to-sequence neural machine translation model that incorporates AMR parsing and graph recurrent neural networks to enhance Chinese–Mongolian translation. The model, which they validated on the Linguistic Data Consortium2019T07 and The 5th China Workshop on Machine Translation datasets, significantly outperformed statistical and sequence-based baselines in Bilingual Evaluation Understudy metrics by enriching source-language semantic encoding and strengthening structural preservation. Experimental results confirmed its efficacy in low-resource scenarios and verified that it reduced semantic loss during translation. The authors also explored pedagogical implications and demonstrated how AMR-enhanced neural machine translation supports differentiated translation pedagogy: by providing reference outputs for novice learners and enabling post-editing exercises for advanced students. Technical limitations in handling specialized terminology and computational demands are discussed, and recommendations for balanced human–machine collaboration in translation education are offered.
As artificial intelligence (AI) continues to influence pedagogical practices, intelligent systems are being integrated into music instruction to support student creativity, responsiveness, and harmonic understanding. The system utilizes deep learning models and multilayer neural networks to analyze sheet music, recognize playing styles, and generate adaptive harmonic progressions. It also provides real-time feedback to meet diverse learner needs. The system architecture incorporates goal setting, tiered instructional guidance, and performance evaluation tools, effectively blending traditional music pedagogy with modern AI technologies. Implemented in a structured music learning environment, the case demonstrates how the integration of AI improves students' improvisational abilities, enhances accompaniment quality, and supports efficient teaching. Challenges such as system latency, timbral limitations, and interface usability are also discussed, offering practical insights for future development and classroom adoption.
Cloud computing has become a key driver in higher education reform, especially for college English online course platforms. Traditional platforms face challenges such as uneven resource distribution, limited computing power, and high operational costs. Cloud computing, with its flexible scalability, resource sharing, and high concurrency processing capabilities, offers a new solution for the integration, distribution, and dynamic scheduling of English teaching resources. This paper explores the application of cloud computing in college English online course platforms, proposes an optimized model for system operation and resource allocation, and verifies its effectiveness through case studies and experiments. The results show that cloud computing enhances the platform's scalability and user experience and that it supports the modernization and intelligent development of college English education. Future work will focus on enhancing personalized services, protecting data privacy, and innovating operational models for sustainable development.
With the development of artificial intelligence, traditional music curricula for music education majors at Chinese vocational colleges can no longer meet diverse teaching needs. This article focuses on the deep integration of artificial intelligence and music teaching; through in-depth analysis of existing music teaching modes, it finds shortcomings in traditional curricula. Based on this analysis, the article constructs a music curriculum suitable for the actual needs of Chinese vocational colleges and highlights the advantages of artificial intelligence technology. The actual effect of the proposed curriculum was verified with experiments showing that the newly constructed music curriculum effectively stimulated students' learning enthusiasm, improved students' music skills and practical abilities, and improved students' innovative thinking and overall quality. This study provides a reference for curriculum construction in higher vocational normal colleges and has theoretical significance and practical value.
With the convergence of AI and learning analytics, business English training is shifting from a content-driven to a learner-driven approach. This study designed an adaptive learning framework that enhanced workplace communication skills using large-scale behavioral data, emotional corpora, and professional scenarios. The system employed multimodal algorithms to create real-time learner profiles and a reinforcement learning scheduler to adjust task difficulty and feedback. A knowledge graph-powered resource engine provided on-demand microlearning and optimized content via closed-loop assessment. The framework integrated linguistic accuracy, communicative effectiveness, and professional relevance, surpassing traditional one-way platforms. Findings showed that multichannel data enriched system perception and hierarchical strategies adapted flexibly to tasks. Scenario-based evaluation enabled contextual progress tracking. This research offered cost-effective corporate training strategies with implications for global remote collaboration and digital workplaces.
In a market-driven economy, enterprises face financial risks from complex internal and external factors, which are more prominent under economic globalization. This paper adopts backpropagation neural networks to address the nonlinearity of corporate financial risk assessment, leveraging its strong fitting ability to develop a financial risk early warning system. Focusing on Chinese enterprises and taking the new energy industry as an empirical sample, the study constructs a graded early warning framework (instead of the traditional binary model) and integrates financial and non-financial indicators to enrich the evaluation system. It advances the theoretical and practical research of financial risk management in China's market context, enhances the accuracy of financial risk prediction for Chinese enterprises, fills the gap of lacking targeted early warning models, and provides new technical tools and insights for improving corporate financial stability in a globalized economy.
In the dissemination and teaching of classical Chinese dance, accurate identification and personalized recommendation of dance styles have always been a challenge. Existing research has relied heavily on manual observation and subjective judgment, which is not only inefficient but also struggles to meet users' personalized needs. This study proposed a Chinese classical dance style recognition and personalized recommendation system that integrated deep learning and reinforcement learning. An efficient recognition framework for dance styles was constructed using convolutional neural networks and transformer models, significantly improving the accuracy and efficiency of style recognition. In addition, a personalized recommendation model was designed based on reinforcement learning to make the recommended content more accurate and personalized. The research results not only provide strong support for the digital inheritance of Chinese classical dance but also provide innovative ideas for intelligent applications in cultural and artistic fields.
This paper examines how intelligent sensor-assisted teaching affects students' physical and motor intelligence within an educational system that combines the Analytic Hierarchy Process (AHP) and fuzzy assessment techniques. An experimental design was implemented in a structured format to evaluate student development, teaching, and learning outcomes in sensor-assisted instruction. The relative importance of the evaluation indicators was determined using the AHP method, and fuzzy modeling was applied to address uncertainty in the performance evaluation process. The findings reveal that smart sensor-based instruction is highly effective in developing students holistically, including motor and cognitive domains. Also, the strategy enhances teachers' teaching performance by helping them learn and apply theoretical and practical skills. All in all, the research demonstrates the topicality of the combined approach of integrating intelligent sensing technologies with systematic assessment tools in the contemporary educational setting.
This paper constructs a decision-support framework comprising data, rules, interventions, and feedback for ecological education, addressing the demand to integrate healthy consumption upgrading and ecological education. Based on students' physical measurement data over the past three years, this paper introduces transaction compression, hash pruning, and information entropy approximation to improve the Apriori algorithm, mines correlations among physical fitness indicators, and analyzes the current state of teaching practice using teachers' questionnaires. The results show that the improved algorithm can significantly enhance operational efficiency while maintaining rule consistency and effectively identify stable correlations between speed and strength and other key physical fitness indicators. The research shows that the data-driven method can provide a basis for operable teaching interventions and resource allocation for the ecological reform of physical education classrooms, and offer a reproducible case path for implementing ecological education in college and university physical education courses.
This paper explores a neural network system designed to optimize film visual effects in mid-sized studios. Using a deep learning approach with convolutional neural networks and attention mechanisms, the system addresses inefficiencies in manual workflows, such as inconsistent actor–computer-generated image interactions and excessive lighting rework. The case study focuses on the 2024 fantasy short Mountain of Mist, produced by Nanjing’s Cloud Frame VFX Studio, with a ¥500,000 budget. The system utilizes cloud deployment on Amazon Web Services p3.8xlarge instances and human–artificial intelligence collaboration in which artificial intelligence generates base frames and artists add creative refinements. For key visual effects elements like dynamic “ghost mist” and bioluminescent undergrowth, the system reduced production time by 85% and lighting rework by 83.3%, while achieving theatrical-grade quality (peak signal-to-noise ratio of 34.5 db; structural similarity index of 0.94). The findings demonstrate the system’s potential to enhance precision, reduce costs, and improve scalability for mid-sized studios.
