In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users,this paper takes microblog as the research object to study the detection of privacy disclosure ...In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users,this paper takes microblog as the research object to study the detection of privacy disclosure in social networks.First,we perform fast privacy leak detection on the currently published text based on the fastText model.In the case that the text to be published contains certain private information,we fully consider the aggregation effect of the private information leaked by different channels,and establish a convolution neural network model based on multi-dimensional features(MF-CNN)to detect privacy disclosure comprehensively and accurately.The experimental results show that the proposed method has a higher accuracy of privacy disclosure detection and can meet the real-time requirements of detection.展开更多
Real-world objects exhibit intricate semantic properties that can be characterized from a multitude of perspectives, which necessitates the development of a model capable of discerning multiple patterns within data, w...Real-world objects exhibit intricate semantic properties that can be characterized from a multitude of perspectives, which necessitates the development of a model capable of discerning multiple patterns within data, while concurrently predicting several Labeling Dimensions (LDs) — a task known as Multi-dimensional Classification (MDC). While the class imbalance issue has been extensively investigated within the multi-class paradigm, its study in the MDC context has been limited due to the imbalance shift phenomenon. A sample’s classification as a minor or major class instance becomes ambiguous when it belongs to a minor class in one LD and a major class in another. Previous MDC methodologies predominantly emphasized instance-wise criteria, neglecting prediction capabilities from a dimension aspect, i.e., the average classification performance across LDs. We assert the significance of dimension-wise metrics in real-world MDC applications and introduce two such metrics. Furthermore, we observe imbalanced class distributions within each LD and propose a novel Imbalance-Aware fusion Model (IMAM) for addressing the MDC problem. Specifically, we first decompose the task into multiple multi-class classification problems, creating imbalance-aware deep models for each LD separately. This straightforward method performs well across LDs without sacrificing performance in instance-wise criteria. Subsequently, we employ LD-wise models as multiple teachers and transfer their knowledge across all LDs to a unified student model. Experimental results on several real-world datasets demonstrate that our IMAM approach excels in both instance-wise evaluations and the proposed dimension-wise metrics.展开更多
多维分类(multi-dimensional classification,MDC)模型在处理高维特征时面临计算效率与泛化性能的挑战。特征选择通过筛选有效特征子集,可同时降低维度并提升分类器性能。已有的MDC研究主要集中于显式地建模类空间之间的依赖关系,而面向...多维分类(multi-dimensional classification,MDC)模型在处理高维特征时面临计算效率与泛化性能的挑战。特征选择通过筛选有效特征子集,可同时降低维度并提升分类器性能。已有的MDC研究主要集中于显式地建模类空间之间的依赖关系,而面向MDC的特征选择方法仍需深入探索。针对MDC数据的特点,设计了一种互信息与遗传算法融合的多维分类特征选择算法MIGA(multi-dimensional classification feature selection algorithm based on fusion of mutual information and genetic algorithm)。该算法设计基于类空间综合相关性的种群初始化策略,以增加种群的多样性并加速收敛;提出自适应变异策略,依据特征综合得分动态调整变异概率以平衡全局探索与局部开发能力;融合MDC三项指标构建负加权和形式的适应度函数以适配GA优化框架。在10个MDC数据集上的实验结果表明:相较于特征映射降维方法(PCA、MDS)、监督式MDC降维方法SDeM(supervised dimensionality reduction for MDC)以及专用于MDC的过滤式特征选择算法MIFS(mutual information feature selection),MIGA所获特征子集显著提升了多维分类模型的泛化性能。展开更多
In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces ...In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features.In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features.Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.展开更多
Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale th...Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.展开更多
Teaching quality is the core guarantee for universities to achieve their talent cultivation goals,and teaching supervision,as an important means of monitoring teaching quality,runs through the entire process of teachi...Teaching quality is the core guarantee for universities to achieve their talent cultivation goals,and teaching supervision,as an important means of monitoring teaching quality,runs through the entire process of teaching management.Based on the teaching quality report of School A at University Z for the autumn semester of 2024,this paper systematically analyzes the current situation,problems,and causes of the teaching supervision mechanism through multi-dimensional data analysis of expert classroom observation,peer evaluation,and classroom feedback.On this basis,combined with the application prospects of artificial intelligence technology,it proposes paths to optimize the teaching supervision mechanism,including improving the classroom observation feedback mechanism,increasing supervision coverage,and strengthening the linkage between feedback and teaching reform,providing practical experience and theoretical support for improving teaching quality in universities.展开更多
With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have ev...With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.61672101)the Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(ICDDXN004)Key Lab of Information Network Security,Ministry of Public Security,China(No.C18601).
文摘In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users,this paper takes microblog as the research object to study the detection of privacy disclosure in social networks.First,we perform fast privacy leak detection on the currently published text based on the fastText model.In the case that the text to be published contains certain private information,we fully consider the aggregation effect of the private information leaked by different channels,and establish a convolution neural network model based on multi-dimensional features(MF-CNN)to detect privacy disclosure comprehensively and accurately.The experimental results show that the proposed method has a higher accuracy of privacy disclosure detection and can meet the real-time requirements of detection.
基金supported by the National Key R&D Program of China(2020AAA0109401,2020AAA0109405),(62376118,62006112,62250069,62206245)the Young Elite Scientists Sponsorship Program of Jiangsu Association for Science and the Technology 2021-020Collaborative Innovation Center of Novel Software Technology and Industrialization.
文摘Real-world objects exhibit intricate semantic properties that can be characterized from a multitude of perspectives, which necessitates the development of a model capable of discerning multiple patterns within data, while concurrently predicting several Labeling Dimensions (LDs) — a task known as Multi-dimensional Classification (MDC). While the class imbalance issue has been extensively investigated within the multi-class paradigm, its study in the MDC context has been limited due to the imbalance shift phenomenon. A sample’s classification as a minor or major class instance becomes ambiguous when it belongs to a minor class in one LD and a major class in another. Previous MDC methodologies predominantly emphasized instance-wise criteria, neglecting prediction capabilities from a dimension aspect, i.e., the average classification performance across LDs. We assert the significance of dimension-wise metrics in real-world MDC applications and introduce two such metrics. Furthermore, we observe imbalanced class distributions within each LD and propose a novel Imbalance-Aware fusion Model (IMAM) for addressing the MDC problem. Specifically, we first decompose the task into multiple multi-class classification problems, creating imbalance-aware deep models for each LD separately. This straightforward method performs well across LDs without sacrificing performance in instance-wise criteria. Subsequently, we employ LD-wise models as multiple teachers and transfer their knowledge across all LDs to a unified student model. Experimental results on several real-world datasets demonstrate that our IMAM approach excels in both instance-wise evaluations and the proposed dimension-wise metrics.
文摘多维分类(multi-dimensional classification,MDC)模型在处理高维特征时面临计算效率与泛化性能的挑战。特征选择通过筛选有效特征子集,可同时降低维度并提升分类器性能。已有的MDC研究主要集中于显式地建模类空间之间的依赖关系,而面向MDC的特征选择方法仍需深入探索。针对MDC数据的特点,设计了一种互信息与遗传算法融合的多维分类特征选择算法MIGA(multi-dimensional classification feature selection algorithm based on fusion of mutual information and genetic algorithm)。该算法设计基于类空间综合相关性的种群初始化策略,以增加种群的多样性并加速收敛;提出自适应变异策略,依据特征综合得分动态调整变异概率以平衡全局探索与局部开发能力;融合MDC三项指标构建负加权和形式的适应度函数以适配GA优化框架。在10个MDC数据集上的实验结果表明:相较于特征映射降维方法(PCA、MDS)、监督式MDC降维方法SDeM(supervised dimensionality reduction for MDC)以及专用于MDC的过滤式特征选择算法MIFS(mutual information feature selection),MIGA所获特征子集显著提升了多维分类模型的泛化性能。
基金supported by National Science Foundation of China (No. 62176055)China University S&T Innovation Plan Guided by the Ministry of Education。
文摘In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features.In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features.Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2012CB315805 and the National Natural Science Foundation of China under Grant Nos. 61472130 and 61702174.
文摘Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.
基金Research on the Quality Verification of Applied University Talent Cultivation Based on the“Five Degrees,”a major project of university-level talent training quality and teaching reform in 2023(TFCJGZD202303)。
文摘Teaching quality is the core guarantee for universities to achieve their talent cultivation goals,and teaching supervision,as an important means of monitoring teaching quality,runs through the entire process of teaching management.Based on the teaching quality report of School A at University Z for the autumn semester of 2024,this paper systematically analyzes the current situation,problems,and causes of the teaching supervision mechanism through multi-dimensional data analysis of expert classroom observation,peer evaluation,and classroom feedback.On this basis,combined with the application prospects of artificial intelligence technology,it proposes paths to optimize the teaching supervision mechanism,including improving the classroom observation feedback mechanism,increasing supervision coverage,and strengthening the linkage between feedback and teaching reform,providing practical experience and theoretical support for improving teaching quality in universities.
基金supported by the Science and Technology Project of the Headquarters of the State Grid Corporation(project code:5400-202323233A-1-1-ZN).
文摘With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.
