Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinct...Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
Objective: The purpose of this study was to compare long-term stability and satisfaction between orthodontic camouflage and orthognathic surgery in treatment of moderate skeletal Class III adults. Materials and Method...Objective: The purpose of this study was to compare long-term stability and satisfaction between orthodontic camouflage and orthognathic surgery in treatment of moderate skeletal Class III adults. Materials and Methods: A total of 25 adults females who had been treated with orthodontic camouflage for Class III malocclusions were recalled at least 3 years post-treatment to evaluate stability and satisfaction with treatment outcomes. The data were compared with similar data for long-term outcomes in 21 patients with the same Class III problems who had bimaxillary surgical correction. Results: In the camouflage patients, small mean changes in skeletal landmark positions occurred over the long term, although the changes were generally much smaller than in the surgery patients. Dental changes in the surgery group were more severe than those in the camouflage group. The camouflage patients reported fewer functional or temporomandibular joint problems than did the surgery patients. Both groups reported similar levels of overall satisfaction with treatment. Conclusion: The results suggest that both camouflage and surgical treatment in moderate skeletal Class III adults can achieve satisfactory outcomes and provide long-term stability. If patients do not readily accept surgery because of potential surgical complications or financial difficulties, camouflage treatment may be an effective alternative treatment.展开更多
Information systems and information technology (IS/IT) play an important role in supporting the organization to conduct its business processes. The research was conducted at the University of Satya Christian Wacana (S...Information systems and information technology (IS/IT) play an important role in supporting the organization to conduct its business processes. The research was conducted at the University of Satya Christian Wacana (SWCU). Business processes in perceived walking SWCU are not optimal in using IS/IT in the organization. The planning process/IT has not been done in all business units and SWCU does not have an Executive Information System that can help in the decision making process. The process of strategic planning of information systems and information technology using The Open Group Architecture Framework (TOGAF) has compiled a few methods of strategic planning. The results obtained by the study of the needs of IS/IT in SWCU generating application portfolio will be implemented in the institution.展开更多
Based on the existing disadvantages of traditional teaching mode of Satellite Meteorology course in Chengdu University of Information Technology and the new teaching mode overview of“Internet Plus flipped class”,thi...Based on the existing disadvantages of traditional teaching mode of Satellite Meteorology course in Chengdu University of Information Technology and the new teaching mode overview of“Internet Plus flipped class”,this paper is mainly to construct flipped class teaching mode of Satellite Meteorology under the background of“Internet Plus”in Chengdu University of Information Technology.Through the Internet,it also tries to integrate this teaching mode into flipped class before class,in class,after class,which means to provide a new thought for the teaching of this course in colleges and universities.展开更多
波动方程系数矩阵对称化是整合不同类别波动方程、降低波传播模拟难度的有效方法,目前已成功应用于声波方程、各向同性与各向异性介质弹性波动方程。该研究将推导出双项介质波动方程的系数矩阵对称式;随后,引入多轴完全匹配层,采用迎风...波动方程系数矩阵对称化是整合不同类别波动方程、降低波传播模拟难度的有效方法,目前已成功应用于声波方程、各向同性与各向异性介质弹性波动方程。该研究将推导出双项介质波动方程的系数矩阵对称式;随后,引入多轴完全匹配层,采用迎风格式分部求和-一致逼近项(summation by parts-simultaneous approximation terms,SBP-SAT)有限差分方法离散波动方程,并通过能量法进行稳定性评估。通过数值仿真,表明所提出的离散框架具有整合度高,稳定性好和拓展性强等特点。此外,该方法可以稳定模拟曲线域中的波传播并降低其实现成本,表明了波动方程系数矩阵对称化方法及其离散框架在波传播模拟领域具有广泛的应用前景。展开更多
师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同...师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同源性与实践契合性。CLASS视域下师幼互动面临情感支持的缺乏、课堂组织的失衡、教学支持的脱节的现实困境。基于此,提出情感交融,以情育情;协调秩序,灵活组织;回应需求,弥合引导的创新路径。The quality of teacher-child interaction is not only a key element of the quality of kindergarten education, but also an important aspect of the connotative development of pre-school education in China. Using literature, logical analysis and other methods, we analyze the associated logic, dilemma and innovative path of teacher-child interaction from the perspective of CLASS theory. The study found that the logic of CLASS theory and teacher-child interaction lies in the homology of the theory and the fit of practice, and that teacher-child interaction in the CLASS perspective faces the dilemmas of lack of emotional support, imbalance of classroom organization, and disconnection of pedagogical support. Based on this, we propose innovative paths of emotional integration, nurturing emotions with emotions, coordinating order, flexible organization, responding to needs, and bridging guidance.展开更多
文摘Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
文摘Objective: The purpose of this study was to compare long-term stability and satisfaction between orthodontic camouflage and orthognathic surgery in treatment of moderate skeletal Class III adults. Materials and Methods: A total of 25 adults females who had been treated with orthodontic camouflage for Class III malocclusions were recalled at least 3 years post-treatment to evaluate stability and satisfaction with treatment outcomes. The data were compared with similar data for long-term outcomes in 21 patients with the same Class III problems who had bimaxillary surgical correction. Results: In the camouflage patients, small mean changes in skeletal landmark positions occurred over the long term, although the changes were generally much smaller than in the surgery patients. Dental changes in the surgery group were more severe than those in the camouflage group. The camouflage patients reported fewer functional or temporomandibular joint problems than did the surgery patients. Both groups reported similar levels of overall satisfaction with treatment. Conclusion: The results suggest that both camouflage and surgical treatment in moderate skeletal Class III adults can achieve satisfactory outcomes and provide long-term stability. If patients do not readily accept surgery because of potential surgical complications or financial difficulties, camouflage treatment may be an effective alternative treatment.
文摘Information systems and information technology (IS/IT) play an important role in supporting the organization to conduct its business processes. The research was conducted at the University of Satya Christian Wacana (SWCU). Business processes in perceived walking SWCU are not optimal in using IS/IT in the organization. The planning process/IT has not been done in all business units and SWCU does not have an Executive Information System that can help in the decision making process. The process of strategic planning of information systems and information technology using The Open Group Architecture Framework (TOGAF) has compiled a few methods of strategic planning. The results obtained by the study of the needs of IS/IT in SWCU generating application portfolio will be implemented in the institution.
文摘Let function f(z) be analytic in |z| <1 and continuous on |z |≤1. The saturation class for the Jackson sums of the Bessel series of f(z) is discussed.
基金This paper is the research achievement of The Teaching Mode Research and Practice of“Internet Plus Flipped Class”——Taking the Course of Satellite Meteorology as Example(JY2018083)the education and teaching research and reform project of Chengdu University of Information Technology in 2018.
文摘Based on the existing disadvantages of traditional teaching mode of Satellite Meteorology course in Chengdu University of Information Technology and the new teaching mode overview of“Internet Plus flipped class”,this paper is mainly to construct flipped class teaching mode of Satellite Meteorology under the background of“Internet Plus”in Chengdu University of Information Technology.Through the Internet,it also tries to integrate this teaching mode into flipped class before class,in class,after class,which means to provide a new thought for the teaching of this course in colleges and universities.
文摘波动方程系数矩阵对称化是整合不同类别波动方程、降低波传播模拟难度的有效方法,目前已成功应用于声波方程、各向同性与各向异性介质弹性波动方程。该研究将推导出双项介质波动方程的系数矩阵对称式;随后,引入多轴完全匹配层,采用迎风格式分部求和-一致逼近项(summation by parts-simultaneous approximation terms,SBP-SAT)有限差分方法离散波动方程,并通过能量法进行稳定性评估。通过数值仿真,表明所提出的离散框架具有整合度高,稳定性好和拓展性强等特点。此外,该方法可以稳定模拟曲线域中的波传播并降低其实现成本,表明了波动方程系数矩阵对称化方法及其离散框架在波传播模拟领域具有广泛的应用前景。
文摘师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同源性与实践契合性。CLASS视域下师幼互动面临情感支持的缺乏、课堂组织的失衡、教学支持的脱节的现实困境。基于此,提出情感交融,以情育情;协调秩序,灵活组织;回应需求,弥合引导的创新路径。The quality of teacher-child interaction is not only a key element of the quality of kindergarten education, but also an important aspect of the connotative development of pre-school education in China. Using literature, logical analysis and other methods, we analyze the associated logic, dilemma and innovative path of teacher-child interaction from the perspective of CLASS theory. The study found that the logic of CLASS theory and teacher-child interaction lies in the homology of the theory and the fit of practice, and that teacher-child interaction in the CLASS perspective faces the dilemmas of lack of emotional support, imbalance of classroom organization, and disconnection of pedagogical support. Based on this, we propose innovative paths of emotional integration, nurturing emotions with emotions, coordinating order, flexible organization, responding to needs, and bridging guidance.
