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
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
Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural n...Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.展开更多
The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineerin...The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineering,Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications,Nanchang University,Nanchang 330031,China",and"School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China",respectively.The order of the two affiliations are not correct.展开更多
The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychologi...The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.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
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
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
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
Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only f...Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method.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
Objectives This study aimed to analyse the benefits of a gamified clinical supervision strategy during hospital-based training,particularly regarding the development of critical and reflective thinking among undergrad...Objectives This study aimed to analyse the benefits of a gamified clinical supervision strategy during hospital-based training,particularly regarding the development of critical and reflective thinking among undergraduate nursing students.Methods From April to July 2023,second-year nursing students who undertaking a nine-week clinical placement in a cardiology ward in northern Portugal were selected.Following a two-week diagnostic phase,students participated in a six-week gamified supervision programme comprising weekly 60-min sessions:infection-control decision-making;technical-procedural reasoning;guided emotional and ethical reflection;and clinical reasoning quiz on cardiology topics.Students completed weekly Structured Reflection Guide entries;supervisors recorded structured field notes after each session;and,after the intervention,students answered a post-intervention questionnaire and participated in focus groups.Qualitative data(reflections,field notes,open-ended questionnaire items,and focus-group transcripts)were analyzed using Bardin’s content analysis;quantitative questionnaire items were summarized descriptively.Results All seven students completed the six gamified sessions and submitted weekly reflection entries.Five students(71.4%)completed the questionnaire.Across data sources,students reported that gamified activities supported knowledge consolidation,teamwork,and clinical reasoning.Questionnaire data showed that all respondents(n=5,100%)strongly agreed that gamification enhanced their learning and should be maintained in clinical training.Reflections and focus groups revealed recurring themes related to emotional expression,sense of belonging,and difficulties using structured reflection tools,particularly in terms of comprehension and timing.Conclusion The gamified supervision strategy integrated into clinical training provided structured opportunities for practical engagement,collaborative work,and guided reflection.These findings suggest that gamification may support the development of reflective and critical-thinking processes in authentic clinical environments.展开更多
Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant...Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant’s eight-step concept analysis approach was utilized to identify and define the attributes,antecedents,and consequences of reflective supervision in the ICU.An extensive literature search was conducted across various databases,including Google Scholar,CINAHL,PubMed.Articles published from 2005 to 2025 were identified.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)2020 statement to indicate the included articles and extract related data based on relevance.Results Forty articles were included in the analysis.The identified attributes included the supervisor-supervisee relationship,effective communication,teamwork,collaborations,reflection,competencies,feedback,continuous support,and autonomous choice.The identified antecedents included participation,supportive supervision,flexibility,open-door policy,training,and motivation.Consequences impacting the success of reflective supervision were identified as promotion of resiliency,autonomy,work-life balance,self-awareness,increased self-esteem,professional development,critical thinking,increased job satisfaction,and enhanced commitment.Conclusions Reflective supervision is a complex professional self-care strategy that enhances ICU practice,by promoting nurses’well-being,self-awareness,therapeutic skills,and professional development.展开更多
The development of electrocatalysts that both work effectively at industrial current density and resist chloride ion(Cl^(-))corrosion remains a key challenge for hydrogen production from Cl^(-)-rich alkaline water.Her...The development of electrocatalysts that both work effectively at industrial current density and resist chloride ion(Cl^(-))corrosion remains a key challenge for hydrogen production from Cl^(-)-rich alkaline water.Herein,we report a CrO_(x)-engineered nickel-based oxide catalyst(FeCoCrO_(x)/NF)that achieves exceptional activity and stability through a dual-functional interfacial mechanism.Combing in situ Raman spectroscopy,18O isotopic labeling,and electrochemical analysis,we demonstrate that the oxygen evolution reaction follows a lattice oxygen-mediated mechanism.The CrO_(x)layer selectively adsorbs hydroxide ions,forming a dynamic interfacial barrier that electrostatically repels Cl^(-)ingress,thereby mitigating Cl^(-)corrosion.Through enthalpy-based analysis,we demonstrate that electronic redistribution via Cr-O-Fe bonding increases the vacancy formation energy of Fe,thereby suppressing its dissolution.In alkaline electrolyte containing 0.5 M Cl^(-)(1.0 M KOH),the catalyst is operating continuously for 1400 h at an industrial current density of 1000 mA cm^(-2).Furthermore,the catalyst retains 99.5%of its initial activity under fluctuating current density(100-1000 mA cm^(-2)),demonstrating robustness required for industrial electrolyzers.This study establishes a paradigm for designing corrosion-resistant electrocatalysts through the synergistic modulation of interfacial ion selectivity and bulk lattice oxygen activation,advancing the application of green hydrogen production in Cl^(-)-rich alkaline water.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.
文摘Weakly supervised semantic segmentation(WSSS)is a tricky task,which only provides category information for segmentation prediction.Thus,the key stage of WSSS is to generate the pseudo labels.For convolutional neural network(CNN)based methods,in which class activation mapping(CAM)is proposed to obtain the pseudo labels,and only concentrates on the most discriminative parts.Recently,transformer-based methods utilize attention map from the multi-headed self-attention(MHSA)module to predict pseudo labels,which usually contain obvious background noise and incoherent object area.To solve the above problems,we use the Conformer as our backbone,which is a parallel network based on convolutional neural network(CNN)and Transformer.The two branches generate pseudo labels and refine them independently,and can effectively combine the advantages of CNN and Transformer.However,the parallel structure is not close enough in the information communication.Thus,parallel structure can result in poor details about pseudo labels,and the background noise still exists.To alleviate this problem,we propose enhancing convolution CAM(ECCAM)model,which have three improved modules based on enhancing convolution,including deeper stem(DStem),convolutional feed-forward network(CFFN)and feature coupling unit with convolution(FCUConv).The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches.After experimental verification,the improved modules we propose can help the network perceive more local information from images,making the final segmentation results more refined.Compared with similar architecture,our modules greatly improve the semantic segmentation performance and achieve70.2%mean intersection over union(mIoU)on the PASCAL VOC 2012 dataset.
文摘The authors regret that there were errors in the affiliations and the funding declaration in the original published version.The affiliations a and b of the original manuscript are"School of Information Engineering,Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications,Nanchang University,Nanchang 330031,China",and"School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China",respectively.The order of the two affiliations are not correct.
基金supported by the National Social Science Funds of China(Grant No.21BSH050)Major Project of National Social Science Funds of China(Grant No.20&ZD013).
文摘The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.
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.
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.
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.
基金supported by the National Natural Science Foundation of China(Grants 62176097,61433007)Fundamental Research Funds for the Central Universities(Grant 2019kfyXKJC024)the 111 Project on Computational Intelligence and Intelligent Control(Grant B18024).
文摘Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method.
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Objectives This study aimed to analyse the benefits of a gamified clinical supervision strategy during hospital-based training,particularly regarding the development of critical and reflective thinking among undergraduate nursing students.Methods From April to July 2023,second-year nursing students who undertaking a nine-week clinical placement in a cardiology ward in northern Portugal were selected.Following a two-week diagnostic phase,students participated in a six-week gamified supervision programme comprising weekly 60-min sessions:infection-control decision-making;technical-procedural reasoning;guided emotional and ethical reflection;and clinical reasoning quiz on cardiology topics.Students completed weekly Structured Reflection Guide entries;supervisors recorded structured field notes after each session;and,after the intervention,students answered a post-intervention questionnaire and participated in focus groups.Qualitative data(reflections,field notes,open-ended questionnaire items,and focus-group transcripts)were analyzed using Bardin’s content analysis;quantitative questionnaire items were summarized descriptively.Results All seven students completed the six gamified sessions and submitted weekly reflection entries.Five students(71.4%)completed the questionnaire.Across data sources,students reported that gamified activities supported knowledge consolidation,teamwork,and clinical reasoning.Questionnaire data showed that all respondents(n=5,100%)strongly agreed that gamification enhanced their learning and should be maintained in clinical training.Reflections and focus groups revealed recurring themes related to emotional expression,sense of belonging,and difficulties using structured reflection tools,particularly in terms of comprehension and timing.Conclusion The gamified supervision strategy integrated into clinical training provided structured opportunities for practical engagement,collaborative work,and guided reflection.These findings suggest that gamification may support the development of reflective and critical-thinking processes in authentic clinical environments.
文摘Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant’s eight-step concept analysis approach was utilized to identify and define the attributes,antecedents,and consequences of reflective supervision in the ICU.An extensive literature search was conducted across various databases,including Google Scholar,CINAHL,PubMed.Articles published from 2005 to 2025 were identified.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)2020 statement to indicate the included articles and extract related data based on relevance.Results Forty articles were included in the analysis.The identified attributes included the supervisor-supervisee relationship,effective communication,teamwork,collaborations,reflection,competencies,feedback,continuous support,and autonomous choice.The identified antecedents included participation,supportive supervision,flexibility,open-door policy,training,and motivation.Consequences impacting the success of reflective supervision were identified as promotion of resiliency,autonomy,work-life balance,self-awareness,increased self-esteem,professional development,critical thinking,increased job satisfaction,and enhanced commitment.Conclusions Reflective supervision is a complex professional self-care strategy that enhances ICU practice,by promoting nurses’well-being,self-awareness,therapeutic skills,and professional development.
基金supported by the National Nature Science Foundation of China under Grant No.22269021the Tianshan Talent Project of Xinjiang Uygur Autonomous Region:2023TSYCQNTJ0039the Open project of Key Laboratory in Xinjiang Uygur Autonomous Region of China:2023D04027。
文摘The development of electrocatalysts that both work effectively at industrial current density and resist chloride ion(Cl^(-))corrosion remains a key challenge for hydrogen production from Cl^(-)-rich alkaline water.Herein,we report a CrO_(x)-engineered nickel-based oxide catalyst(FeCoCrO_(x)/NF)that achieves exceptional activity and stability through a dual-functional interfacial mechanism.Combing in situ Raman spectroscopy,18O isotopic labeling,and electrochemical analysis,we demonstrate that the oxygen evolution reaction follows a lattice oxygen-mediated mechanism.The CrO_(x)layer selectively adsorbs hydroxide ions,forming a dynamic interfacial barrier that electrostatically repels Cl^(-)ingress,thereby mitigating Cl^(-)corrosion.Through enthalpy-based analysis,we demonstrate that electronic redistribution via Cr-O-Fe bonding increases the vacancy formation energy of Fe,thereby suppressing its dissolution.In alkaline electrolyte containing 0.5 M Cl^(-)(1.0 M KOH),the catalyst is operating continuously for 1400 h at an industrial current density of 1000 mA cm^(-2).Furthermore,the catalyst retains 99.5%of its initial activity under fluctuating current density(100-1000 mA cm^(-2)),demonstrating robustness required for industrial electrolyzers.This study establishes a paradigm for designing corrosion-resistant electrocatalysts through the synergistic modulation of interfacial ion selectivity and bulk lattice oxygen activation,advancing the application of green hydrogen production in Cl^(-)-rich alkaline water.
