In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,...In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,focusing on its influence on learning outcomes and student engagement.The research uses a quantitative approach,utilizing surveys and academic performance data to evaluate two main objectives:(1)the impact of AI teaching methods on academic performance compared to traditional instruction;(2)the level of student engagement and satisfaction with AI-based learning tools.The study sample includes undergraduate students from multiple universities in China,allowing for a diverse representation of various disciplines.Data will be collected through standardized tests,questionnaires,and academic records,ensuring the reliability and validity of the results.The findings will provide insights into the potential advantages and challenges of AI integration in higher education and inform future strategies for adopting AI in Chinese classrooms.By exploring both the academic and practical aspects of AI-driven education,this research aims to contribute valuable knowledge to the growing field of AI in education,particularly in the context of Chinese higher education.The results are expected to have implications for educators,policymakers,and AI developers interested in enhancing the effectiveness of educational technologies.展开更多
Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variabil...Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.展开更多
The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potentia...The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.展开更多
Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects.The primary challenge is to integrate diverse pharmacophores within a singl...Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects.The primary challenge is to integrate diverse pharmacophores within a single-molecule framework.To address this,we introduced DeepSA,a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine,a wellknown local anesthetic.DeepSA integrates deep neural networks into metaheuristics,effectively constraining molecular space during compound generation.This framework employs a sophisticated objective function that accounts for scaffold preservation,anti-inflammatory properties,and covalent constraints.Through a sequence of local editing to navigate the molecular space,DeepSA successfully identified AT-17,a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models.Mechanistic insights into AT-17 revealed its dual mode of action:selective inhibition of NaV1.7 and 1.8 channels,contributing to its prolonged local anesthetic effects,and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway.These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery,particularly within stringent chemical constraints.展开更多
文摘In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,focusing on its influence on learning outcomes and student engagement.The research uses a quantitative approach,utilizing surveys and academic performance data to evaluate two main objectives:(1)the impact of AI teaching methods on academic performance compared to traditional instruction;(2)the level of student engagement and satisfaction with AI-based learning tools.The study sample includes undergraduate students from multiple universities in China,allowing for a diverse representation of various disciplines.Data will be collected through standardized tests,questionnaires,and academic records,ensuring the reliability and validity of the results.The findings will provide insights into the potential advantages and challenges of AI integration in higher education and inform future strategies for adopting AI in Chinese classrooms.By exploring both the academic and practical aspects of AI-driven education,this research aims to contribute valuable knowledge to the growing field of AI in education,particularly in the context of Chinese higher education.The results are expected to have implications for educators,policymakers,and AI developers interested in enhancing the effectiveness of educational technologies.
文摘Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.
文摘The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.
基金supported by the National Natural Science Foundation of China(82273784,China)the Research and Develop Program,West China Hospital of Stomatology Sichuan University(RD-03-202004,China)+3 种基金the 1.3.5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(ZYYC 21002,ZYGD23025,China)the Clinical Research Innovation Project,West China Hospital,Sichuan University(2019 HXCX006,China)the Science and Technology Major Project of Tibetan Autonomous Region of China(XZ202201ZD0001G,China)the Sichuan Science and Technology Program(2023 ZYD0168,China).
文摘Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects.The primary challenge is to integrate diverse pharmacophores within a single-molecule framework.To address this,we introduced DeepSA,a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine,a wellknown local anesthetic.DeepSA integrates deep neural networks into metaheuristics,effectively constraining molecular space during compound generation.This framework employs a sophisticated objective function that accounts for scaffold preservation,anti-inflammatory properties,and covalent constraints.Through a sequence of local editing to navigate the molecular space,DeepSA successfully identified AT-17,a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models.Mechanistic insights into AT-17 revealed its dual mode of action:selective inhibition of NaV1.7 and 1.8 channels,contributing to its prolonged local anesthetic effects,and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway.These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery,particularly within stringent chemical constraints.
