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。

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.

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.

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.

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.

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.

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.

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.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable routes.Traditional approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall prevention.However,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing faces.This study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application gap.It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,respectively.These results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their safety.Additionally,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition tasks.This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

Mindfulness training in medical education as a means to improve resilience,empathy,and mental health in the medical profession

The high rates of depression,burnout,and increased risk of suicide among medical students,residents,and physicians in comparison with other careers signal a mental health crisis within our profession.We contend that this crisis coupled with the inadequate acquisition of interpersonal skills during medical education results from the interaction between a challenging environment and the mental capital of individuals.Additionally,we posit that mindfulness-based practices are instrumental for the development of major components of mental capital,such as resilience,flexibility of mind,and learning skills,while also serving as a pathway to enhance empathy,compassion,self-awareness,conflict resolution,and relational abilities.Importantly,the evidence base supporting the effectiveness of mindfulness-based interventions has been increasing over the years,and a growing number of medical schools have already integrated mindfulness into their curricula.While we acknowledge that mindfulness is not a panacea for all educational and mental health problems in this field,we argue that there is currently an unprecedented opportunity to gather momentum,spread and study mindfulness-based programs in medical schools around the world as a way to address some longstanding shortcomings of the medical profession and the health and educational systems upon which it is rooted.

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.