In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and ...In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.展开更多
Hierarchical Text Classification(HTC)aims to match text to hierarchical labels.Existing methods overlook two critical issues:first,some texts cannot be fully matched to leaf node labels and need to be classified to th...Hierarchical Text Classification(HTC)aims to match text to hierarchical labels.Existing methods overlook two critical issues:first,some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target.Second,error propagation occurs when a misclassification at a parent node propagates down the hierarchy,ultimately leading to inaccurate predictions at the leaf nodes.To address these limitations,we propose an uncertainty-guided HTC depth-aware model called DepthMatch.Specifically,we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels,classifying them into the corresponding parent node labels.This approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate uncertainty.Experimental results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets:WOS(Web of Science),RCV1-V2(Reuters Corpus Volume I),AAPD(Arxiv Academic Paper Dataset),and BGC.Notably,on the BGC dataset,it improvesMicro-F1 andMacro-F1 scores by at least 1.09%and 1.74%,respectively.展开更多
Urbanization and industrialization have escalated water pollution,threatening ecosystems and human health.Water pollution not only degrades water quality but also poses long-term risks to human health through the food...Urbanization and industrialization have escalated water pollution,threatening ecosystems and human health.Water pollution not only degrades water quality but also poses long-term risks to human health through the food chain.The development of efficient wastewater detection and treatment methods is essential for mitigating this environmental hazard.Carbon dots(CDs),as emerging carbon-based nanomaterials,exhibit properties such as biocompatibility,photoluminescence(PL),water solubility,and strong adsorption,positioning them as promising candidates for environmental monitoring and management.Particularly in wastewater treatment,their optical and electron transfer properties make them ideal for pollutant detection and removal.Despite their potential,comprehensive reviews on CDs'role in wastewater treatment are scarce,often lacking detailed insights into their synthesis,PL mechanisms,and practical applications.This review systematically addresses the synthesis,PL mechanisms,and wastewater treatment applications of CDs,aiming to bridge existing research gaps.It begins with an overview of CDs structure and classification,essential for grasping their properties and uses.The paper then explores the pivotal PL mechanisms of CDs,crucial for their sensing capabilities.Next,comprehensive synthesis strategies are presented,encompassing both top-down and bottom-up strategies such as arc discharge,chemical oxidation,and hydrothermal/solvothermal synthesis.The diversity of these methods highlights the potential for tailored CDs production to suit specific environmental applications.Furthermore,the review systematically discusses the applications of CDs in wastewater treatment,including sensing,inorganic removal,and organic degradation.Finally,it delves into the research prospects and challenges of CDs,proposing future directions to enhance their role in wastewater treatment.展开更多
As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimizat...As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.展开更多
Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault sampl...Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.展开更多
Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network training.Howeve...Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network training.However,existing studies have focused solely on adding perturbations to the input,such as text sentences and embedded representations,resulting in adversarial samples that are very similar to the original ones.Such adversarial samples can not significantly improve the diversity of training data,which restricts the potential for improved classification performance.To alleviate the problem,in this paper,we extend the diversity of generated adversarial samples based on the fact that adding different disturbances between different layers of neural network has different effects.We propose a novel neural network with perturbation strategy(PTNet),which generates adversarial samples by adding perturbation to the intrinsic representation of each hidden layer of the neural network.Specifically,we design two different perturbation ways to perturb each hidden layer:1)directly adding a certain threshold perturbation;2)adding the perturbation in the way of adversarial training.Through above settings,we can get more perturbed intrinsic representations of hidden layers and use them as new adversarial samples,thus improving the diversity of the augmented training data.We validate the effectiveness of our approach on six text classification datasets and demonstrate that it improves the classification ability of the model.In particular,the classification accuracy on the sentiment analysis task improved by an average of 1.79%and on question classification task improved by 3.2%compared to the BERT baseline,respectively.展开更多
Purpose： Based on our experience of designing and testing a computer-based game for teaching undergraduate students information literacy （IL） concepts and skills, this paper summarizes the basic strategies for stri...Purpose： Based on our experience of designing and testing a computer-based game for teaching undergraduate students information literacy （IL） concepts and skills, this paper summarizes the basic strategies for striking a balance between education and entertainment for the designers of quality IL games. Design/methodology/approach： The project team recruited 10 college students to play the game and post-game group interviews revealed problems and optimization priorities. The optimized game was tested among 50 college students. Based on a comparison of testing results of the two versions of the game, basic strategies for designing quality 1L games were summarized. Findings： The following 5 basic strategies can effectively promote combination of education and entertainment： l） using adventure games to enhance gaming experience, 2） plotting an intriguing story to attract players, 3） motivating players to engage in game play with game components such as challenge, curiosity, fantasy and control, 4） presenting learning materials through game props, and 5） assigning players tasks to be completed with subject knowledge. Research limitations： The 5 basic strategies have been tested only in the development process of one game, and the book classification knowledge in the mini-game is limited to the 22 major categories of the Chinese Library Classification. Practical implications： University libraries may refer to our experience to design and utilize educational games to promote the IL education for college students. Originality value： Few empirical studies tested and summarized strategies for combining learning and fun in the design of IL games for university students. The 5 strategies, which are summarized in the process of design and optimization of the mini-game book classification, are valuable for other designers of IL games.展开更多
近年来,甘蔗病毒病频繁流行暴发,其危害日益严重。为全面了解甘蔗病毒侵染及其致病机制,以“甘蔗”“病毒”为关键词,在中国知网和Web of Science数据库中检索2015—2025年甘蔗病毒病相关文献中文6篇,英文107篇,总结了近10年的甘蔗病毒...近年来,甘蔗病毒病频繁流行暴发,其危害日益严重。为全面了解甘蔗病毒侵染及其致病机制,以“甘蔗”“病毒”为关键词,在中国知网和Web of Science数据库中检索2015—2025年甘蔗病毒病相关文献中文6篇,英文107篇,总结了近10年的甘蔗病毒发生情况及为害我国甘蔗生产的多种病毒,对不同类型的病毒粒子特性、基因组结构、发病症状、传播介体以及致病机制等进行综合分析,并就转基因技术、RNAi技术、基因编辑技术、新型药物开发以及智能化育种技术等在甘蔗抗病毒育种和病害防控中的应用前景进行展望。结果表明:1)可侵染甘蔗的病毒种类丰富,复合感染严重,目前我国甘蔗品种对病毒病总体抗性较差;2)甘蔗病毒病的发生与多种因素密切相关,包括品种抗性、环境条件及病毒传播途径;3)利用生物技术手段,例如转基因、RNAi、基因编辑和抗病毒药物研发等,有望提升甘蔗的抗病能力。综上,甘蔗病毒病的防治需要综合考虑多种因素,结合传统方法、现代生物技术手段,利用智能化育种策略筛选优势种质,以实现甘蔗的抗病、高糖、高产的最终目标。展开更多
基金supported by:the 2023 Basic Public Welfare Research Project of the Wenzhou Science and Technology Bureau“Research on Multi-Source Data Classification and Grading Standards and Intelligent Algorithms for Higher Education Institutions”(Project No.G2023094)Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions(Grant/Award Number:2024QN061)2023 Basic Public Welfare Research Project of Wenzhou(No.:S2023014).
文摘In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.
基金sponsored by the National Key Research and Development Program of China(No.2021YFF0704100)the National Natural Science Foundation of China(No.62136002)+1 种基金the Chongqing Natural Science Foundation(No.cstc2022ycjh-bgzxm0004)the Science and Technology Commission of Chongqing Municipality(CSTB2023NSCQ-LZX0006),respectively.
文摘Hierarchical Text Classification(HTC)aims to match text to hierarchical labels.Existing methods overlook two critical issues:first,some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target.Second,error propagation occurs when a misclassification at a parent node propagates down the hierarchy,ultimately leading to inaccurate predictions at the leaf nodes.To address these limitations,we propose an uncertainty-guided HTC depth-aware model called DepthMatch.Specifically,we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels,classifying them into the corresponding parent node labels.This approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate uncertainty.Experimental results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets:WOS(Web of Science),RCV1-V2(Reuters Corpus Volume I),AAPD(Arxiv Academic Paper Dataset),and BGC.Notably,on the BGC dataset,it improvesMicro-F1 andMacro-F1 scores by at least 1.09%and 1.74%,respectively.
基金supported by the Natural Science Foundation of Hebei Province(No.E2022208046)National Science Foundation of China(No.52004080)+2 种基金Key project of National Natural Science Foundation of China(No.U20A20130)Key research and development project of Hebei Province(No.22373704D)2023 Central Government Guide Local Science and Technology Development Fund Project(No.236Z1812 G)。
文摘Urbanization and industrialization have escalated water pollution,threatening ecosystems and human health.Water pollution not only degrades water quality but also poses long-term risks to human health through the food chain.The development of efficient wastewater detection and treatment methods is essential for mitigating this environmental hazard.Carbon dots(CDs),as emerging carbon-based nanomaterials,exhibit properties such as biocompatibility,photoluminescence(PL),water solubility,and strong adsorption,positioning them as promising candidates for environmental monitoring and management.Particularly in wastewater treatment,their optical and electron transfer properties make them ideal for pollutant detection and removal.Despite their potential,comprehensive reviews on CDs'role in wastewater treatment are scarce,often lacking detailed insights into their synthesis,PL mechanisms,and practical applications.This review systematically addresses the synthesis,PL mechanisms,and wastewater treatment applications of CDs,aiming to bridge existing research gaps.It begins with an overview of CDs structure and classification,essential for grasping their properties and uses.The paper then explores the pivotal PL mechanisms of CDs,crucial for their sensing capabilities.Next,comprehensive synthesis strategies are presented,encompassing both top-down and bottom-up strategies such as arc discharge,chemical oxidation,and hydrothermal/solvothermal synthesis.The diversity of these methods highlights the potential for tailored CDs production to suit specific environmental applications.Furthermore,the review systematically discusses the applications of CDs in wastewater treatment,including sensing,inorganic removal,and organic degradation.Finally,it delves into the research prospects and challenges of CDs,proposing future directions to enhance their role in wastewater treatment.
基金supported by the National Natural Science Foundation of China(61876089,61403206,61876185,61902281)the opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS302)+2 种基金the Natural Science Foundation of Jiangsu Province(BK20141005)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(14KJB520025)the Engineering Research Center of Digital Forensics,Ministry of Education,and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.
文摘Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.
基金supported by Project of China National Intellectual Property Administration(No.220134).
文摘Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network training.However,existing studies have focused solely on adding perturbations to the input,such as text sentences and embedded representations,resulting in adversarial samples that are very similar to the original ones.Such adversarial samples can not significantly improve the diversity of training data,which restricts the potential for improved classification performance.To alleviate the problem,in this paper,we extend the diversity of generated adversarial samples based on the fact that adding different disturbances between different layers of neural network has different effects.We propose a novel neural network with perturbation strategy(PTNet),which generates adversarial samples by adding perturbation to the intrinsic representation of each hidden layer of the neural network.Specifically,we design two different perturbation ways to perturb each hidden layer:1)directly adding a certain threshold perturbation;2)adding the perturbation in the way of adversarial training.Through above settings,we can get more perturbed intrinsic representations of hidden layers and use them as new adversarial samples,thus improving the diversity of the augmented training data.We validate the effectiveness of our approach on six text classification datasets and demonstrate that it improves the classification ability of the model.In particular,the classification accuracy on the sentiment analysis task improved by an average of 1.79%and on question classification task improved by 3.2%compared to the BERT baseline,respectively.
基金supported by the National Social Science Foundation of China (Grant No.: 13BTQ024) the Foundation for Humanities and Social Sciences of the Chinese Ministry of Education (Grant No.: 12YJAZH155)
文摘Purpose： Based on our experience of designing and testing a computer-based game for teaching undergraduate students information literacy （IL） concepts and skills, this paper summarizes the basic strategies for striking a balance between education and entertainment for the designers of quality IL games. Design/methodology/approach： The project team recruited 10 college students to play the game and post-game group interviews revealed problems and optimization priorities. The optimized game was tested among 50 college students. Based on a comparison of testing results of the two versions of the game, basic strategies for designing quality 1L games were summarized. Findings： The following 5 basic strategies can effectively promote combination of education and entertainment： l） using adventure games to enhance gaming experience, 2） plotting an intriguing story to attract players, 3） motivating players to engage in game play with game components such as challenge, curiosity, fantasy and control, 4） presenting learning materials through game props, and 5） assigning players tasks to be completed with subject knowledge. Research limitations： The 5 basic strategies have been tested only in the development process of one game, and the book classification knowledge in the mini-game is limited to the 22 major categories of the Chinese Library Classification. Practical implications： University libraries may refer to our experience to design and utilize educational games to promote the IL education for college students. Originality value： Few empirical studies tested and summarized strategies for combining learning and fun in the design of IL games for university students. The 5 strategies, which are summarized in the process of design and optimization of the mini-game book classification, are valuable for other designers of IL games.
文摘近年来,甘蔗病毒病频繁流行暴发,其危害日益严重。为全面了解甘蔗病毒侵染及其致病机制,以“甘蔗”“病毒”为关键词,在中国知网和Web of Science数据库中检索2015—2025年甘蔗病毒病相关文献中文6篇,英文107篇,总结了近10年的甘蔗病毒发生情况及为害我国甘蔗生产的多种病毒,对不同类型的病毒粒子特性、基因组结构、发病症状、传播介体以及致病机制等进行综合分析,并就转基因技术、RNAi技术、基因编辑技术、新型药物开发以及智能化育种技术等在甘蔗抗病毒育种和病害防控中的应用前景进行展望。结果表明:1)可侵染甘蔗的病毒种类丰富,复合感染严重,目前我国甘蔗品种对病毒病总体抗性较差;2)甘蔗病毒病的发生与多种因素密切相关,包括品种抗性、环境条件及病毒传播途径;3)利用生物技术手段,例如转基因、RNAi、基因编辑和抗病毒药物研发等,有望提升甘蔗的抗病能力。综上,甘蔗病毒病的防治需要综合考虑多种因素,结合传统方法、现代生物技术手段,利用智能化育种策略筛选优势种质,以实现甘蔗的抗病、高糖、高产的最终目标。
