Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of t...Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.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
Background Evidence on the effects of different exercise interventions on cognitive function is insufficient.Aims To evaluate the feasibility and effects of remotely supervised aerobic exercise(AE)and resistance exerc...Background Evidence on the effects of different exercise interventions on cognitive function is insufficient.Aims To evaluate the feasibility and effects of remotely supervised aerobic exercise(AE)and resistance exercise(RE)interventions in older adults with mild cognitive impairment(MCI).Methods This study is a 6-month pilot three-arm randomised controlled trial.Eligible participants(n=108)were recruited and randomised to the AE group,RE group or control(CON)group with a 1:1:1 ratio.Interventions were delivered at home with remote supervision.We evaluated participants’global cognition,memory,executive function,attention,physical activity levels,physical performance and muscle strength of limbs at baseline,3 months(T1)and 6 months(T2)after randomisation.A linear mixed-effects model was adopted for data analyses after controlling for covariates.Tukey’s method was used for adjusting for multiple comparisons.Sensitivity analyses were performed after excluding individuals with low compliance rates.Results 15(13.89%)participants dropped out.The median compliance rates in the AE group and RE group were 67.31%and 93.27%,respectively.After adjusting for covariates,the scores of the Alzheimer’s Disease Assessment Scale-Cognitive subscale in the AE group decreased by 2.04(95%confidence interval(CI)−3.41 to−0.67,t=−2.94,p=0.004)and 1.53(95%CI−2.88 to−0.17,t=−2.22,p=0.028)points more than those in the CON group at T1 and T2,respectively.The effects of AE were still significant at T1(estimate=−1.70,95%CI−3.20 to−0.21,t=−2.69,p=0.021),but lost statistical significance at T2 after adjusting for multiple comparisons.As for executive function,the Stroop time interference in the RE group decreased by 11.76 s(95%CI−21.62 to−1.90,t=−2.81,p=0.015)more than that in the AE group at T2 after Tukey’s adjustment.No other significant effects on cognitive functions were found.Conclusions Both remotely supervised AE and RE programmes are feasible in older adults with MCI.AE has positive effects on global cognition,and RE improves executive function.展开更多
Drawing upon self-determination theory,this study examines the effects of vicarious abusive supervision on third-party’s self-efficacy and task performance within organizational contexts.Data were collected via surve...Drawing upon self-determination theory,this study examines the effects of vicarious abusive supervision on third-party’s self-efficacy and task performance within organizational contexts.Data were collected via surveys from 337 employees across diverse organizations.The results indicate that vicarious abusive supervision significantly undermines both self-efficacy and task performance among employees who are indirectly exposed to such behavior but not directly targeted.Furthermore,self-efficacy serves as a mediator between vicarious abusive supervision and task performance;however,this mediating effect is attenuated for employees with a high promotion focus.These findings provide valuable theoretical and practical insights,particularly in the domain of organizational behavior,by emphasizing the critical role of promotion focus in mitigating the negative effects of vicarious abusive supervision.This research contributes to the organizational behavior literature by shifting the focus from the traditional supervisor-subordinate dynamic to a third-party perspective,thereby enriching our understanding of how vicarious abusive supervision impacts employees within organizational settings.The study underscores the importance of self-efficacy and promotion focus as key factors in unethical leadership contexts.展开更多
Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applicati...Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.展开更多
The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gaine...The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.展开更多
With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness...With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.展开更多
Because methane is flammable and explosive,the detection process is time-consuming and dangerous,and it is difficult to obtain labeled data.In order to reduce the dependence on marker data when detecting methane conce...Because methane is flammable and explosive,the detection process is time-consuming and dangerous,and it is difficult to obtain labeled data.In order to reduce the dependence on marker data when detecting methane concentration using tunable diode laser absorption spectroscopy(TDLAS)technology,this paper designs a methane gas acquisition platform based on TDLAS and proposes a methane gas concentration detection model based on semi-supervised learning.Firstly,the methane gas is feature extracted,and then semi-supervised learning is introduced to select the optimal feature combination;subsequently,the traditional whale optimization algorithm is improved to optimize the parameters of the random forest to detect the methane gas concentration.The results show that the model is not only able to select the optimal feature combination under limited labeled data,but also has an accuracy of 94.25%,which is better than the traditional model,and is robust in terms of parameter optimization.展开更多
Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly ...Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.展开更多
The federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning...The federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning being difficult to process large-scale unlabeled data. The existing federated self-supervision framework has problems with low communication efficiency and high communication delay between clients and central servers. Therefore, we added edge servers to the federated self-supervision framework to reduce the pressure on the central server caused by frequent communication between both ends. A communication compression scheme using gradient quantization and sparsification was proposed to optimize the communication of the entire framework, and the algorithm of the sparse communication compression module was improved. Experiments have proved that the learning rate changes of the improved sparse communication compression module are smoother and more stable. Our communication compression scheme effectively reduced the overall communication overhead.展开更多
Without the dependence of depth ground truth,self‐supervised learning is a promising alternative to train monocular depth estimation.It builds its own supervision signal with the help of other tools,such as view synt...Without the dependence of depth ground truth,self‐supervised learning is a promising alternative to train monocular depth estimation.It builds its own supervision signal with the help of other tools,such as view synthesis and pose networks.However,more training parameters and time consumption may be involved.This paper proposes a monocular depth prediction framework that can jointly learn the depth value and pose transformation between images in an end‐to‐end manner.The depth network creatively employs an asymmetric convolution block instead of every square kernel layer to strengthen the learning ability of extracting image features when training.During infer-ence time,the asymmetric kernels are fused and converted to the original network to predict more accurate image depth,thus bringing no extra computations anymore.The network is trained and tested on the KITTI monocular dataset.The evaluated results demonstrate that the depth model outperforms some State of the Arts(SOTA)ap-proaches and can reduce the inference time of depth prediction.Additionally,the pro-posed model performs great adaptability on the Make3D dataset.展开更多
Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous human...Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.展开更多
Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully superv...Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.展开更多
Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved throu...Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.展开更多
An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the ...An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.展开更多
基金support by the National Natural Science Foundation of China(NSFC)under grant number 61873274.
文摘Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.
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.
基金funded by the National Natural Science Foundation of China(81871854,72374014)the National Key R&D Program of China(2020YFC2008804)+1 种基金the Shanghai Jiao Tong University Young Talent Cultivation Program in Liberal Arts(2024QN041)the Shanghai Jiao Tong University School of Medicine:Nursing Development Program(SJTUHLXK2024).
文摘Background Evidence on the effects of different exercise interventions on cognitive function is insufficient.Aims To evaluate the feasibility and effects of remotely supervised aerobic exercise(AE)and resistance exercise(RE)interventions in older adults with mild cognitive impairment(MCI).Methods This study is a 6-month pilot three-arm randomised controlled trial.Eligible participants(n=108)were recruited and randomised to the AE group,RE group or control(CON)group with a 1:1:1 ratio.Interventions were delivered at home with remote supervision.We evaluated participants’global cognition,memory,executive function,attention,physical activity levels,physical performance and muscle strength of limbs at baseline,3 months(T1)and 6 months(T2)after randomisation.A linear mixed-effects model was adopted for data analyses after controlling for covariates.Tukey’s method was used for adjusting for multiple comparisons.Sensitivity analyses were performed after excluding individuals with low compliance rates.Results 15(13.89%)participants dropped out.The median compliance rates in the AE group and RE group were 67.31%and 93.27%,respectively.After adjusting for covariates,the scores of the Alzheimer’s Disease Assessment Scale-Cognitive subscale in the AE group decreased by 2.04(95%confidence interval(CI)−3.41 to−0.67,t=−2.94,p=0.004)and 1.53(95%CI−2.88 to−0.17,t=−2.22,p=0.028)points more than those in the CON group at T1 and T2,respectively.The effects of AE were still significant at T1(estimate=−1.70,95%CI−3.20 to−0.21,t=−2.69,p=0.021),but lost statistical significance at T2 after adjusting for multiple comparisons.As for executive function,the Stroop time interference in the RE group decreased by 11.76 s(95%CI−21.62 to−1.90,t=−2.81,p=0.015)more than that in the AE group at T2 after Tukey’s adjustment.No other significant effects on cognitive functions were found.Conclusions Both remotely supervised AE and RE programmes are feasible in older adults with MCI.AE has positive effects on global cognition,and RE improves executive function.
文摘Drawing upon self-determination theory,this study examines the effects of vicarious abusive supervision on third-party’s self-efficacy and task performance within organizational contexts.Data were collected via surveys from 337 employees across diverse organizations.The results indicate that vicarious abusive supervision significantly undermines both self-efficacy and task performance among employees who are indirectly exposed to such behavior but not directly targeted.Furthermore,self-efficacy serves as a mediator between vicarious abusive supervision and task performance;however,this mediating effect is attenuated for employees with a high promotion focus.These findings provide valuable theoretical and practical insights,particularly in the domain of organizational behavior,by emphasizing the critical role of promotion focus in mitigating the negative effects of vicarious abusive supervision.This research contributes to the organizational behavior literature by shifting the focus from the traditional supervisor-subordinate dynamic to a third-party perspective,thereby enriching our understanding of how vicarious abusive supervision impacts employees within organizational settings.The study underscores the importance of self-efficacy and promotion focus as key factors in unethical leadership contexts.
文摘Background Zonal application maps are designed to represent field variability using key variables that can be translated into tailored management practices.For cotton,zonal maps for crop growth regulator(CGR)applications under variable-rate(VR)strategies are commonly based exclusively on vegetation indices(VIs)variability.However,VIs often saturate in dense crop vegetation areas,limiting their effectiveness in distinguishing variability in crop growth.This study aimed to compare unsupervised framework(UF)and supervised framework(SUF)approaches for generat-ing zonal application maps for CGR under VR conditions.During 2022-2023 agricultural seasons,an UF was employed to generate zonal maps based on locally collected field data on plant height of cotton,satellite imagery,soil texture,and phenology data.Subsequently,a SUF(based on historical data between 2020-2021 to 2022-2023 agricultural seasons)was developed to predict plant height using remote sensing and phenology data,aiming to replicate same zonal maps but without relying on direct field measurements of plant height.Both approaches were tested in three fields and on two different dates per field.Results The predictive model for plant height of SUF performed well,as indicated by the model metrics.However,when comparing zonal application maps for specific field-date combinations,the predicted plant height exhibited lower variability compared with field measurements.This led to variable compatibility between SUF maps,which utilized the model predictions,and the UF maps,which were based on the real field data.Fields characterized by much pronounced soil texture variability yielded the highest compatibility between the zonal application maps produced by both SUF and UF approaches.This was predominantly due to the greater consistency in estimating plant development patterns within these heterogeneous field environments.While VR application approach can facilitate product savings during the application operation,other key factors must be considered.These include the availability of specialized machinery required for this type of applications,as well as the inherent operational costs associated with applying a single CGR product which differs from the typical uniform rate applications that often integrate multi-ple inputs.Conclusion Predictive modeling shows promise for assisting in the creation of zonal application maps for VR of CGR applications.However,the degree of agreement with the actual variability in crop growth found in the field should be evaluated on a field-by-field basis.The SUF approach,which is based on plant heigh prediction,demonstrated potential for supporting the development of zonal application maps for VR of CGR applications.However,the degree to which this approach aligns itself with the actual variability in crop growth observed in the field may vary,necessi-tating field-by-field evaluation.
基金funding from the following sources:National Natural Science Foundation of China(U1904119)Research Programs of Henan Science and Technology Department(232102210054)+3 种基金Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0070)Henan Province Key Research and Development Project(231111212000)Aviation Science Foundation(20230001055002)supported by Henan Center for Outstanding Overseas Scientists(GZS2022011).
文摘The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset.
基金supported by the Natural Science Foundation Project of Fujian Province,China(Grant No.2023J011439 and No.2019J01859).
文摘With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.
基金supported by the Ministry of Education Chunhui Program of China(No.HZKY20220304).
文摘Because methane is flammable and explosive,the detection process is time-consuming and dangerous,and it is difficult to obtain labeled data.In order to reduce the dependence on marker data when detecting methane concentration using tunable diode laser absorption spectroscopy(TDLAS)technology,this paper designs a methane gas acquisition platform based on TDLAS and proposes a methane gas concentration detection model based on semi-supervised learning.Firstly,the methane gas is feature extracted,and then semi-supervised learning is introduced to select the optimal feature combination;subsequently,the traditional whale optimization algorithm is improved to optimize the parameters of the random forest to detect the methane gas concentration.The results show that the model is not only able to select the optimal feature combination under limited labeled data,but also has an accuracy of 94.25%,which is better than the traditional model,and is robust in terms of parameter optimization.
文摘Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.
文摘The federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning being difficult to process large-scale unlabeled data. The existing federated self-supervision framework has problems with low communication efficiency and high communication delay between clients and central servers. Therefore, we added edge servers to the federated self-supervision framework to reduce the pressure on the central server caused by frequent communication between both ends. A communication compression scheme using gradient quantization and sparsification was proposed to optimize the communication of the entire framework, and the algorithm of the sparse communication compression module was improved. Experiments have proved that the learning rate changes of the improved sparse communication compression module are smoother and more stable. Our communication compression scheme effectively reduced the overall communication overhead.
基金Natural Science Foundation of Shanghai,Grant/Award Number:61922063National Key R&D Program of China,Grant/Award Number:2018YFB1305003+2 种基金Fundamental Research Funds for the Central UniversitiesShanghai Hong Kong Macao Taiwan Science and Technology Cooperation Project,Grant/Award Number:21550760900Shanghai Municipal Science and Technology Major Project,Grant/Award Number:2021SHZDZX0100。
文摘Without the dependence of depth ground truth,self‐supervised learning is a promising alternative to train monocular depth estimation.It builds its own supervision signal with the help of other tools,such as view synthesis and pose networks.However,more training parameters and time consumption may be involved.This paper proposes a monocular depth prediction framework that can jointly learn the depth value and pose transformation between images in an end‐to‐end manner.The depth network creatively employs an asymmetric convolution block instead of every square kernel layer to strengthen the learning ability of extracting image features when training.During infer-ence time,the asymmetric kernels are fused and converted to the original network to predict more accurate image depth,thus bringing no extra computations anymore.The network is trained and tested on the KITTI monocular dataset.The evaluated results demonstrate that the depth model outperforms some State of the Arts(SOTA)ap-proaches and can reduce the inference time of depth prediction.Additionally,the pro-posed model performs great adaptability on the Make3D dataset.
基金the National Natural Science Foundation of China(42001408,61806097).
文摘Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.
文摘Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.
文摘Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.
基金Supported by National Natural Science Foundation of China (No. 40872193)
文摘An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.
