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
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
Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the...Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the“Five-Dimensional Education”model as an example,the School of Anesthesiology of Wannan Medical College redefines the roles of class advisors as builders of class ecology,leaders of value creation,companions on the growth journey,practitioners of lifelong learning,and connectors of human efforts,forming a comprehensive and multi-dimensional framework for student education management.This model effectively enhances the quality of talent cultivation in anesthesiology and optimizes the efficiency of educational management.By implementing effective assessment mechanisms,it ensures that class advisors can perform ideological and political education and academic guidance in an efficient,high-quality,and orderly manner.This study not only helps to cultivate medical talents with both moral integrity and professional competence,but also provides valuable theoretical and practical references for reforming student management in medical institutions,thereby promoting the sustainable development of medical education.展开更多
In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can...In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.展开更多
Higher vocational college students are suffering more foreign language anxiety in their English learning.Based on this status quo,the writer poses some pedagogical implication from the perspectives of how to create de...Higher vocational college students are suffering more foreign language anxiety in their English learning.Based on this status quo,the writer poses some pedagogical implication from the perspectives of how to create delightful class atmosphere and reduce students' foreign language anxiety.It is hoped that the findings will help students more effectively learn foreign language.展开更多
Education in the new era advocates taking students as the main body and improving students’core literacy on this basis.Subjectivity has also become one of the keys to quality education.Primary school is not only the ...Education in the new era advocates taking students as the main body and improving students’core literacy on this basis.Subjectivity has also become one of the keys to quality education.Primary school is not only the initial stage of life,but also the key period to develop students’subjectivity.At present,there are many problems in class activities,such as the weak concept of taking students as the main body,the lack of subject consciousness among students,the flawed construction system of class activities,the unreasonable evaluation system of class activities,and so on.By establishing the role-playing model under the background of“class activity month,”students can play different roles in class activities and participate in various links,such as theme selection,scheme design,organization,implementation,summary,and evaluation of class activities.Through this process,it does not only improve the quality of class activities,but also cultivate students’subject consciousness and ability in a certain subject,thus highlighting their subject status in class activities.展开更多
Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however...Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.展开更多
Class attendance is important.Class attendance recording is often done using“roll-call”or signing attendance registers.These are time consuming,easy to cheat,and it is difficult to draw any information from them.The...Class attendance is important.Class attendance recording is often done using“roll-call”or signing attendance registers.These are time consuming,easy to cheat,and it is difficult to draw any information from them.There are other,expensive alternatives to automate attendance recording with varying accuracy.This study experimented with a smartphone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully,while keeping the solution cost-effective.The effect of different class sizes was also investigated.The research was done within a pragmatism philosophy,using a prototype in a field experiment.The algorithms that were used are Viola–Jones(Haar features),deep neural network and histogram of oriented gradients for detection,and eigenfaces,fisherfaces,and local binary pattern histogram for recognition.The best combination was Viola–Jones combined with fisherfaces,with a mean accuracy of 54%for a class of 10 students and 34.5%for a class of 22 students.The best all over performance on a single class photo was 70%(class size 10).As is,this prototype is not accurate enough to use,but with a few adjustments,it may become a cheap,easy-to-implement solution to the attendance recording problem.展开更多
In this article, we give an operator transform T （＊） from class A operator to the class of hyponormal operators. It is different from the operator transform T defined by M. Ch and T. Yamazaki. Then, we show that σ...In this article, we give an operator transform T （＊） from class A operator to the class of hyponormal operators. It is different from the operator transform T defined by M. Ch and T. Yamazaki. Then, we show that σ（T ） = σ（ T （＊）） and σa（T ）／{0} = σa（ T （＊））／{0}, in case T belongs to class A. Next, we obtain some relations between T and T （9）.展开更多
In order to deal with the complex association relationships between classes in an object-oriented software system,a novel approach for identifying refactoring opportunities is proposed.The approach can be used to dete...In order to deal with the complex association relationships between classes in an object-oriented software system,a novel approach for identifying refactoring opportunities is proposed.The approach can be used to detect complex and duplicated many-to-many association relationships in source code,and to provide guidance for further refactoring.In the approach,source code is first transformed to an abstract syntax tree from which all data members of each class are extracted,then each class is characterized in connection with a set of association classes saving its data members.Next,classes in common associations are obtained by comparing different association classes sets in integrated analysis.Finally,on condition of pre-defined thresholds,all class sets in candidate for refactoring and their common association classes are saved and exported.This approach is tested on 4 projects.The results show that the precision is over 96%when the threshold is 3,and 100%when the threshold is 4.Meanwhile,this approach has good execution efficiency as the execution time taken for a project with more than 500 classes is less than 4 s,which also indicates that it can be applied to projects of different scales to identify their refactoring opportunities effectively.展开更多
The class A scavenger receptor, encoded by the macrophage scavenger receptor 1 （MSR1） gene, is a pattern recognition receptor （PPR） primarily expressed in macrophages. It has been reported that genetic polymorphis...The class A scavenger receptor, encoded by the macrophage scavenger receptor 1 （MSR1） gene, is a pattern recognition receptor （PPR） primarily expressed in macrophages. It has been reported that genetic polymorphisms of MSR1 are significantly associated with the number of diseased vessels and coronary artery narrowing greater than 20% in Caucasians. However, whether it links genetically to coronary artery disease （CAD） in Chinese is not defined. Here, we performed an independent case-control study in a Chinese population consisting of 402 CAD cases and 400 controls by genotyping ten single nucleotide polymorphisms （SNPs） of MSR1. We found that rs416748 and rs13306541 were significantly associated with an increased risk of CAD with per allele odds ratio （OR） of 1.56 [95% confidence interval （CI） = 1.28-1.90; P 〈 0.001] and 1.70 （95% CI = 1.27-2.27; P 〈 0.001）, re- spectively. Our results indicate that genetic variants of MSR1 may serve as predictive markers for the risk of CAD / in combination with traditional risk factors of CAD in Chinese population.展开更多
Comparing the traditional teaching methods with the modem communicative teaching methods in language theory and also in teaching practice,students can learn much better in an encouraging communicative class atmosphere...Comparing the traditional teaching methods with the modem communicative teaching methods in language theory and also in teaching practice,students can learn much better in an encouraging communicative class atmosphere.The article analyzes the characteristics of an encouraging communicative class atmosphere and provides some practical methods to achieve it.展开更多
Recently, there is greater recognition and increased attempts to protect the rights of irregular workers within Korea and Japan, especially in Korea. This is because of more and more public awareness of the polarizati...Recently, there is greater recognition and increased attempts to protect the rights of irregular workers within Korea and Japan, especially in Korea. This is because of more and more public awareness of the polarization in material conditions between regular workers and irregular workers. So, this study focuses on the main factors explaining awareness of irregular worker issues of each of the classes, and relationship between class consciousness in both countries. The result shows that among factors affecting awareness of irregular work issues, negative effect of subjective employment stability was significant in both countries. In regard of anti-flexibility, while strong class effect was observed in Korea, negative effect of anti-neoliberalism was observed in Japan. This is seemingly contradictory that who opposes neoliberal economic policies agrees with labor market flexibilisation. This phenomenon could be explained by labor market characteristics in Korea and Japan. Japanese labor market is characterized by low flexibility and strong segmentation, while Korean labor market is characterized by high flexibility and strong segmentation. Interaction of these two characteristics increases the labor market inequality in Korea.展开更多
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 Anhui Teaching Quality and Teaching Reform Project(2022jyxm1698)Special Fund Project for Party Construction of Wannan Medical College(WK2024DJ06).
文摘Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the“Five-Dimensional Education”model as an example,the School of Anesthesiology of Wannan Medical College redefines the roles of class advisors as builders of class ecology,leaders of value creation,companions on the growth journey,practitioners of lifelong learning,and connectors of human efforts,forming a comprehensive and multi-dimensional framework for student education management.This model effectively enhances the quality of talent cultivation in anesthesiology and optimizes the efficiency of educational management.By implementing effective assessment mechanisms,it ensures that class advisors can perform ideological and political education and academic guidance in an efficient,high-quality,and orderly manner.This study not only helps to cultivate medical talents with both moral integrity and professional competence,but also provides valuable theoretical and practical references for reforming student management in medical institutions,thereby promoting the sustainable development of medical education.
文摘In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.
文摘Higher vocational college students are suffering more foreign language anxiety in their English learning.Based on this status quo,the writer poses some pedagogical implication from the perspectives of how to create delightful class atmosphere and reduce students' foreign language anxiety.It is hoped that the findings will help students more effectively learn foreign language.
文摘Education in the new era advocates taking students as the main body and improving students’core literacy on this basis.Subjectivity has also become one of the keys to quality education.Primary school is not only the initial stage of life,but also the key period to develop students’subjectivity.At present,there are many problems in class activities,such as the weak concept of taking students as the main body,the lack of subject consciousness among students,the flawed construction system of class activities,the unreasonable evaluation system of class activities,and so on.By establishing the role-playing model under the background of“class activity month,”students can play different roles in class activities and participate in various links,such as theme selection,scheme design,organization,implementation,summary,and evaluation of class activities.Through this process,it does not only improve the quality of class activities,but also cultivate students’subject consciousness and ability in a certain subject,thus highlighting their subject status in class activities.
基金supported by a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 22CTAP-C163951-02).
文摘Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.
文摘Class attendance is important.Class attendance recording is often done using“roll-call”or signing attendance registers.These are time consuming,easy to cheat,and it is difficult to draw any information from them.There are other,expensive alternatives to automate attendance recording with varying accuracy.This study experimented with a smartphone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully,while keeping the solution cost-effective.The effect of different class sizes was also investigated.The research was done within a pragmatism philosophy,using a prototype in a field experiment.The algorithms that were used are Viola–Jones(Haar features),deep neural network and histogram of oriented gradients for detection,and eigenfaces,fisherfaces,and local binary pattern histogram for recognition.The best combination was Viola–Jones combined with fisherfaces,with a mean accuracy of 54%for a class of 10 students and 34.5%for a class of 22 students.The best all over performance on a single class photo was 70%(class size 10).As is,this prototype is not accurate enough to use,but with a few adjustments,it may become a cheap,easy-to-implement solution to the attendance recording problem.
基金supported by Science Foundation of Ministry of Education of China (208081)Technology and pioneering project in Henan Provice (102300410012)Education Foundation of Henan Province (2007110016, 2008B110006)
文摘In this article, we give an operator transform T （＊） from class A operator to the class of hyponormal operators. It is different from the operator transform T defined by M. Ch and T. Yamazaki. Then, we show that σ（T ） = σ（ T （＊）） and σa（T ）／{0} = σa（ T （＊））／{0}, in case T belongs to class A. Next, we obtain some relations between T and T （9）.
文摘In order to deal with the complex association relationships between classes in an object-oriented software system,a novel approach for identifying refactoring opportunities is proposed.The approach can be used to detect complex and duplicated many-to-many association relationships in source code,and to provide guidance for further refactoring.In the approach,source code is first transformed to an abstract syntax tree from which all data members of each class are extracted,then each class is characterized in connection with a set of association classes saving its data members.Next,classes in common associations are obtained by comparing different association classes sets in integrated analysis.Finally,on condition of pre-defined thresholds,all class sets in candidate for refactoring and their common association classes are saved and exported.This approach is tested on 4 projects.The results show that the precision is over 96%when the threshold is 3,and 100%when the threshold is 4.Meanwhile,this approach has good execution efficiency as the execution time taken for a project with more than 500 classes is less than 4 s,which also indicates that it can be applied to projects of different scales to identify their refactoring opportunities effectively.
基金supported by the 973 Project of National Basic Research Program (No. 2012CB517503 and 2011CB503903)National Natural Science Foundation of China (No. 81070120)
文摘The class A scavenger receptor, encoded by the macrophage scavenger receptor 1 （MSR1） gene, is a pattern recognition receptor （PPR） primarily expressed in macrophages. It has been reported that genetic polymorphisms of MSR1 are significantly associated with the number of diseased vessels and coronary artery narrowing greater than 20% in Caucasians. However, whether it links genetically to coronary artery disease （CAD） in Chinese is not defined. Here, we performed an independent case-control study in a Chinese population consisting of 402 CAD cases and 400 controls by genotyping ten single nucleotide polymorphisms （SNPs） of MSR1. We found that rs416748 and rs13306541 were significantly associated with an increased risk of CAD with per allele odds ratio （OR） of 1.56 [95% confidence interval （CI） = 1.28-1.90; P 〈 0.001] and 1.70 （95% CI = 1.27-2.27; P 〈 0.001）, re- spectively. Our results indicate that genetic variants of MSR1 may serve as predictive markers for the risk of CAD / in combination with traditional risk factors of CAD in Chinese population.
文摘Comparing the traditional teaching methods with the modem communicative teaching methods in language theory and also in teaching practice,students can learn much better in an encouraging communicative class atmosphere.The article analyzes the characteristics of an encouraging communicative class atmosphere and provides some practical methods to achieve it.
文摘Recently, there is greater recognition and increased attempts to protect the rights of irregular workers within Korea and Japan, especially in Korea. This is because of more and more public awareness of the polarization in material conditions between regular workers and irregular workers. So, this study focuses on the main factors explaining awareness of irregular worker issues of each of the classes, and relationship between class consciousness in both countries. The result shows that among factors affecting awareness of irregular work issues, negative effect of subjective employment stability was significant in both countries. In regard of anti-flexibility, while strong class effect was observed in Korea, negative effect of anti-neoliberalism was observed in Japan. This is seemingly contradictory that who opposes neoliberal economic policies agrees with labor market flexibilisation. This phenomenon could be explained by labor market characteristics in Korea and Japan. Japanese labor market is characterized by low flexibility and strong segmentation, while Korean labor market is characterized by high flexibility and strong segmentation. Interaction of these two characteristics increases the labor market inequality in Korea.
