融合Mind Map优势助力完善线上线下教学衔接--以园林树木学树种识别教学为例

疫情当前,线上线下混合式教学已经成为课程教学新模式。树种识别是园林树木学教学的重点和难点,也是教学目的之一。由于课程涉及的树种种类繁多,知识点琐碎,专业术语抽象,再加上课时少任务重,使得教师在教学过程中无法将所有树种的特征逐一讲解到,学生在学习过程中也容易混淆,无法有效吸收知识点。因此,在课程的教学改革中,通过引入Mind Map帮助厘清知识框架,优化知识结构,搭建知识关联,不仅使教师授课过程更顺畅,而且能够激发学生在线学习的兴趣,促使学生养成“整理知识点”的良好习惯,使学习效率大幅度提高,从而创建高效的线上课堂,有效巩固混合式教学的教学效果。

基于Mind+软件的口罩识别与无接触测温装置研究与设计

为了减轻疫情防控中工作人员的负担,降低交叉感染的风险,提高防疫工作效率,设计了一种使用Mind+编程的口罩识别与无接触测温装置。该装置以Arduino作为控制平台,采用HuskyLens模块进行口罩识别,使用非接触式温度传感器MLX90614来测量人体表面温度。该装置经过多次测试,口罩识别准确率为95.83%,温度测量的平均相对误差为0.46%,能够自动检查出未正确佩戴口罩或者体温异常的通行人员,并发出报警信号,禁止其入内。从而有效地减少防疫人员的工作量,降低防疫成本。

基于Mind+编程软件的“光照条件影响光合作用强度”实验的改进与创新

在探究“光照条件”对光合作用强度影响的实验中,对光照强度、光质和光照时间等变量的精准控制是实验成败的关键因素。利用Mind+编程软件,实现256种光照强度的变化;在提高光质纯度的同时,实现70种光质的选择;探究任意频闪频率对光合作用强度的影响。本实验实现跨学科融合,体现了探究实验的科学性、实用性和创新性。

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Mindfulness and mindful parenting:Strategies for preschoolers with behavioral issues

The behavior issues of preschoolers are closely related to their parents'parenting styles.This editorial discusses the value and strategies for solving behavior issues in preschoolers from the perspectives of mindfulness and mindful parenting.We expect that upcoming studies will place greater emphasis on the behavioral concerns of preschoolers and the parenting practices that shape them,particularly focusing on proactive interventions for preschoolers'behavioral issues.

The Influential Mechanisms of Theory of Mind on Prosocial Behavior and the Effect of Mindfulness Intervention

Background:Theory of Mind(ToM)and empathy are crucial cognitive and emotional capacities that influence social interactions.While their role in promoting prosocial behavior has been established,the potential moderating effect of mindfulness on this relationship remains unexplored.Understanding these complex interactions is vital for developing effective interventions to foster prosocial behavior among college students.This study examines the influence of ToM on college students’prosocial behavior and explores the moderating role of mindfulness in this relationship.Methods:A mixed-methods approach combining questionnaires and experimental design was employed.Study 1:A survey of 759 college students(mean age 22.03 years;477 females)was conducted using the ToM Scale,Interpersonal Reactivity Index,Prosocial Behavior Tendency Scale,and Mindfulness Awareness Scale.Data were analyzed using correlation and moderated mediation analyses.Study 2:An 8-week mindfulness attention training program was implemented for the intervention group and compared with a control group.Mindfulness training served as a moderating variable to validate Model 59 from Study 1.Results:1.Study 1 found:(a)ToM was significantly positively correlated with prosocial behavior(r=0.31,p<0.01).(b)Empathy partially mediated the relationship between ToM and prosocial behavior(β=0.10,p<0.001,95%CI[0.06,0.14]).(c)Mindfulness negatively moderated the direct path between ToM and three dimensions of prosocial behavior,as well as the indirect path between empathy and kin altruism and reciprocal altruism.Specifically,high levels of mindfulness weakened the direct impact of ToM on prosocial behavior.High levels of mindfulness also weakened the indirect influence of ToM on prosocial behavior through empathy.2.Study 2 results showed:(a)The intervention group had significantly higher levels of trait mindfulness compared to the control group(t=2.56,p<0.05).(b)The validity of the moderated mediation model 59 from Study 1 was verified.Conclusion:While ToM and empathy play crucial roles in fostering prosocial behavior,mindfulness exhibits a more complex influence than anticipated,potentially inhibiting prosocial behavior under certain circumstances.These findings offer novel insights into the psychological mechanisms underlying prosocial behavior and underscore the importance of considering multiple interacting factors in its promotion.

Text Difficulty,Working Memory Capacity and Mind Wandering During Chinese EFL Learners’Reading

This experimental study investigated how text difficulty and different working memory capacity(WMC)affected Chinese EFL learners’reading comprehension and their tendency to engage in task-unrelated thoughts,that is,mind wandering(MW),in the course of reading.Sixty first-year university non-English majors participated in the study.A two-factor mixed experimental design of 2(text difficulty:difficult and simple)×2(WMC:high/large and low/small)was employed.Results revealed that 1)the main and interaction effects of WMC and text difficulty on voluntary MW were significant,whereas those on involuntary MW were not;2)while reading the easy texts,the involuntary MW of high-WMC individuals was less frequent than that of low-WMC ones,whereas while reading the difficult ones,the direct relationship between WMC and involuntary MW was not found;and that 3)high-WMC individuals had a lower overall rate of MW and better reading performance than low-WMC individuals did,but with increasing text difficulty,their rates of overall MW and voluntary MW were getting higher and higher,and the reading performance was getting lower and lower.These results lend support to WM theory and have pedagogical implications for the instruction of L2 reading.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

Mindfulness Facets and Psychological Well-Being among Meditators:Serenity as a Mediating Process

Guided by the theoretical processes of mindfulness and psychological well-being,this study examined serenity as a mediator between mindfulness facets and psychological well-being,as indexed by depressive symptoms and life satisfaction.Participants were 133 mindfulness practitioners who took part in a 3-day transnational meditation event in Hong Kong.Upon informed consent,participants completed a self-report questionnaire.The findings from structural equation modeling showed that serenity mediated the relation between two facets of mindfulness,including describing and nonreacting to inner experience,and life satisfaction.Serenity also mediated the relation between the mindfulness facet of describing and depressive symptoms.Direct associations were indicated between two mindfulness facets,including observing and nonjudging of inner experience,and depressive symptoms.Taken together,the findings revealed mindfulness facets as major correlates of serenity and psychological outcomes among Chinese meditation practitioners.To foster psychological well-being,researchers,educators,and practitioners should pay attention to the role of serenity,describing,and nonreacting to inner experience in mental health.

Confusing Object Detection:A Survey

Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning methodologies.Despite garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures.As of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks.To fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this field.The current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level data.We conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object detection.We also compile and provide detailed descriptions ofwidely used datasets relevant to these detection tasks.Our endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection accuracy.Additionally,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.

Floating Waste Discovery by Request via Object-Centric Learning

Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.

Two-Layer Attention Feature Pyramid Network for Small Object Detection

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

The data analysis of blasting sites has always been the research goal of relevant researchers.The rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of blasting.Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine learning.In this paper,we design a distributedmachine learning training application based on the AWS Lambda platform.Based on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively realized.It also encrypts the data set,effectively reducing the risk of data leakage.We rent a cloud server and a Lambda,and then we conduct experiments to evaluate our applications.Our results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

The Effects of Mindfulness-Based Interventions on Symptoms of Mild Traumatic Brain Injury:A Systematic Review

Mindfulness-based interventions(MBIs)are emerging non-pharmacological treatments for mild traumatic brain injury(mTBI).In this systematic review,the authors aimed to evaluate the potential efficacy of MBIs to provide recommendations for treating patients with mTBI.We searched of the English literature on MBIs for patients with mTBI as of 01 September,2023,using the PubMed,Web of Science,PsycINFO,and Scopus databases.One author performed data extraction and quality scoring of the included literature according to the proposed protocol,and another conducted the review.The review was not registered.A total of 11 studies met the final inclusion criteria,5 of which involved military personnel(veterans).MBIs covered in this review include goal-oriented attention self-regulation(GOALS),mindfulness-based stress reduction(MBSR),acceptance and commitment therapy(ACT),and so on.Research shows that MBSR mainly reduces mental fatigue symptoms in mTBI patients,and GOALS tend to improve their cognitive function.The effect of MBIs on psychological symptoms needs further exploration.Other studies,such as mindfulness-based group therapy and intervention studies targeting mTBI military personnel,are relatively sparse.MBIs have specific effects on mental fatigue and cognitive dysfunction in patients with mTBI.However,the effect on psychological distress and the sustained effectiveness across all symptoms still need further exploration.Considering the particularity of military personnel suffering from mTBI,researchers need to do more intervention studies targeting mTBI military personnel.Therefore,the design of future MBIs trials for mTBI patients’needs to take into account all the factors,such as different populations and severity of traumatic brain injury,to verify the effectiveness of MBIs in alleviating mTBI symptoms and explore the mechanism of intervention.

Mediating Effect of Mindfulness,Self-Esteem and Psychological Resilience in the Relation between Childhood Maltreatment and Life Satisfaction

Childhood maltreatment,as a typical early adverse environment,is known to have a negative impact on one’s life satisfaction.Mindfulness,on the other hand,may serve as a protective factor.This study explored the mediating role of mindfulness and its related variables–positive thoughts,psychological resilience and self-esteem.In order to testify the mechanism,we administered Childhood Trauma Questionnaire(CTQ),Satisfaction with Life Scale(SWLS),Mindful Attention Awareness Scale(MAAS),Connor–Davidson Resilience Scale(CD-RISC)and Rosenberg Self-Esteem Scale(RSES)to a non-clinical sample of Chinese university students(N=1021).The results indicated that positive thoughts did not mediate the relationship between childhood maltreatment and life satisfaction,but self-esteem(β=−0.194,95%CI=[−0.090,−0.040])and psychological resilience(β=−0.063,95%CI=[−0.059,−0.020])mediated the relationship,as well as the“mindfulness-selfesteem”(β=−0.061,95%CI=[−0.287,−0.126])and“mindfulness-psychological resilience”(β=−0.035,95%CI=[−0.115,−0.034]).The results of this study were helpful to understand the relationship between childhood maltreatment and life satisfaction and provided a theoretical basis for the development of mindfulness intervention programs from the perspective of positive psychology.

SMSTracker:A Self-Calibration Multi-Head Self-Attention Transformer for Visual Object Tracking

Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information.However,current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information.In this paper,we introduce self-calibration multi-head self-attention Transformer(SMSTracker)as a solution to these challenges.It employs a hybrid tensor decomposition self-organizing multihead self-attention transformermechanism,which not only compresses and accelerates Transformer operations but also significantly reduces redundant data,thereby enhancing the accuracy and efficiency of tracking.Additionally,we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional trackingmethods,ensuring the stability and reliability of tracking performance across various scenarios.By integrating a hybrid tensor decomposition approach with a self-organizingmulti-head self-attentive transformer mechanism,SMSTracker enhances the efficiency and accuracy of the tracking process.Experimental results show that SMSTracker achieves competitive performance in visual object tracking,promising more robust and efficient tracking systems,demonstrating its potential to providemore robust and efficient tracking solutions in real-world applications.

Learning Discriminatory Information for Object Detection on Urine Sediment Image

In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5.

Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

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.

Improving Transferable Targeted Adversarial Attack for Object Detection Using RCEN Framework and Logit Loss Optimization

Object detection finds wide application in various sectors,including autonomous driving,industry,and healthcare.Recent studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial examples.This not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing systems.Most existing adversarial attack strategies focus primarily on image classification problems,failing to fully exploit the unique characteristics of object detectionmodels,thus resulting in widespread deficiencies in their transferability.Furthermore,previous research has predominantly concentrated on the transferability issues of non-targeted attacks,whereas enhancing the transferability of targeted adversarial examples presents even greater challenges.Traditional attack techniques typically employ cross-entropy as a loss measure,iteratively adjusting adversarial examples to match target categories.However,their inherent limitations restrict their broad applicability and transferability across different models.To address the aforementioned challenges,this study proposes a novel targeted adversarial attack method aimed at enhancing the transferability of adversarial samples across object detection models.Within the framework of iterative attacks,we devise a new objective function designed to mitigate consistency issues arising from cumulative noise and to enhance the separation between target and non-target categories(logit margin).Secondly,a data augmentation framework incorporating random erasing and color transformations is introduced into targeted adversarial attacks.This enhances the diversity of gradients,preventing overfitting to white-box models.Lastly,perturbations are applied only within the specified object’s bounding box to reduce the perturbation range,enhancing attack stealthiness.Experiments were conducted on the Microsoft Common Objects in Context(MS COCO)dataset using You Only Look Once version 3(YOLOv3),You Only Look Once version 8(YOLOv8),Faster Region-based Convolutional Neural Networks(Faster R-CNN),and RetinaNet.The results demonstrate a significant advantage of the proposed method in black-box settings.Among these,the success rate of RetinaNet transfer attacks reached a maximum of 82.59%.