Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is ground...Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is grounded on a prepredicative,i. e. pre-linguistic and pre-communicative,level. I will endorse a Husserlian viewpoint on the issue,and I will try to develop some aspects of the Husserlian account of three-dimensional thing-perception by means of which I will showhowprepredicative experience can actually offer us a fundamental element of our common understanding of objectivity. In doing this,it will be necessary to acknowledge thing-perception as being primarily intertwined with indeterminacy. I will claim that only on the basis of such an intuitive and prepredicative access to the things as partially indeterminate,first,and as determinable,second,is it possible to have an understanding of the world as something (at least partially) independent from the intuition (s) all subjects can have of it. By means of the addition of a consciousness of the thing as accessible to other subjects,one achieves a vision of the thing as fully determinate in itself. This"vision",however,takes one to be aware of the determination of the thing as lying beyond any intuitive grasp of it. The result will,thus,be that the prepredicative constitution of our basic sense of objectivity leads us to intend the world as something which should be accounted for (also) by means of sources different from intuition.展开更多
For a physically possible deformation field of a continuum, the deformation gradient function F can be decomposed into direct sum of a symmetric tensor S and on orthogonal tensor R, which is called S-R decomposition t...For a physically possible deformation field of a continuum, the deformation gradient function F can be decomposed into direct sum of a symmetric tensor S and on orthogonal tensor R, which is called S-R decomposition theorem. In this paper, the S-R decomposition unique existence theorem is proved, by employing matrix and tensor method. Also, a brief proof of its objectivity is given.展开更多
This paper presents an analysis on how objectivity is discursively constructed in journalistic narratives by drawing on the theories of viewpoint and mental space in Cognitive Linguistics. It is posited that at least ...This paper presents an analysis on how objectivity is discursively constructed in journalistic narratives by drawing on the theories of viewpoint and mental space in Cognitive Linguistics. It is posited that at least three mental spaces are projected by a narrative discourse, i.e., a narrated event space, a narrating space, and a basic space, and the distance between the first two spaces determines the degree of objectivity in the narrative discourse. A schema which represents the configuration of the different spaces is proposed and applied in the analysis of journalistic narratives to explore the strategies of objectivity construction. The analysis reveals that what the different journalistic narratives have in common in the construction of objectivity is to distance the narrated event space and the narrating space with the former being foregrounded in the viewpoint arrangement.展开更多
In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Paul...In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Pauline Melville, we argue that Cartesianism has influenced a view on education which tends to consider good and valuable what is ＂scientific＂, ＂objective＂ and ＂universal＂. The subjective and the local seem to be considered undesirable and unreliable. Brazilian scholars on the education field, such as Coracini and Souza are important support for our argument.展开更多
When scientific research began in early twentieth-century China,a key issue was the acquisition of reliable empirical information through objective and precise observations.This article examines a specific case where ...When scientific research began in early twentieth-century China,a key issue was the acquisition of reliable empirical information through objective and precise observations.This article examines a specific case where a scientist grappled with such an issue:the linguist Chao Yuen Ren’s application of mechanical means in his phonetic studies.In the 1920s–1930s,Chao conducted a series of field and lab studies on the dialects in southern and central China.In contrast to traditional scholars’exclusive reliance on sharp ears and rhyme books,Chao employed mechanical devices to inscribe and analyze the spectrographs of dialectical tones and used phonographs to record the articulations of his subjects.It is demonstrated that Chao’s machines not only provided a new method of observation;they also altered the theoretical understanding of certain fundamental categories in Chinese phonology,such as tones.Moreover,Chao did not aim to replace human perception with automatic mechanisms in empirical investigations.Rather,the use of machines in his research called for an active and engaged scientific persona.展开更多
The world is facing a once-in-a-lifetime situation:the COVID-19 pandemic.During the pandemic,the World Health Organization announced an infodemic as well.This infodemic caused infollution and sparked many controversie...The world is facing a once-in-a-lifetime situation:the COVID-19 pandemic.During the pandemic,the World Health Organization announced an infodemic as well.This infodemic caused infollution and sparked many controversies.Pandemics as extraordinary occurrences are always attractive to historians.However,infodemics and biased information threaten objective history-writing.Objectivity as it regards historians is already a much-discussed subject.In this commentary,the fundamental theories about objectivity are delineated.Second,the relationship between the infodemic and COVID-19 pandemic is explained.Lastly,the problems regarding objectivity in the historiography of the COVID-19 pandemic are explored.展开更多
China English, as one of the English varieties, is an objective reality. It is different from Chinglish which is an interlanguage for Chinese English learners. This paper expresses the definition of China English, its...China English, as one of the English varieties, is an objective reality. It is different from Chinglish which is an interlanguage for Chinese English learners. This paper expresses the definition of China English, its objectivity and manifestations in terms of pronunciation, vocabulary, syntax and text.展开更多
The history of science and medicine has long been steeped in the notion that they are objective(untainted by the philosophical and ideological ebbs and flows of society)and utilitarian(doing what is best for the great...The history of science and medicine has long been steeped in the notion that they are objective(untainted by the philosophical and ideological ebbs and flows of society)and utilitarian(doing what is best for the greater good).Because of this,scientific and medical epistemologies and praxis are often held to an esteem that is unquestioned,celebrated,and occasionally unchecked.A closer look at the history of science and medicine,however,readily reveal the extent to which the milieu of society has informed scientific and medical endeavors.As such,an understanding of how the subjectivities of scientific and medical endeavors situate within our contemporary disciplines and practices is significant to one’s ability to truly understand said disciplines.Likewise,such an evaluation will provide insight into our role in perpetuating the illusion of objectivity in these fields.With this in mind,this paper provides a philosophical and historical examination of the concept of objectivity(in contrast to subjectivity)in science and medicine.展开更多
Purpose: The primary aim of this study was to develop an assessment of the fundamental, combined, and complex movement skills required to support childhood physical literacy. The secondary aim was to establish the fea...Purpose: The primary aim of this study was to develop an assessment of the fundamental, combined, and complex movement skills required to support childhood physical literacy. The secondary aim was to establish the feasibility, objectivity, and reliability evidence for the assessment.Methods: An expert advisory group recommended a course format for the assessment that would require children to complete a series of dynamic movement skills. Criterion-referenced skill performance and completion time were the recommended forms of evaluation. Children, 8–12 years of age, self-reported their age and gender and then completed the study assessments while attending local schools or day camps. Face validity was previously established through a Delphi expert(n = 19, 21% female) review process. Convergent validity was evaluated by age and gender associations with assessment performance. Inter-and intra-rater(n = 53, 34% female) objectivity and test–retest(n = 60, 47% female) reliability were assessed through repeated test administration.Results: Median total score was 21 of 28 points(range 5–28). Median completion time was 17 s. Total scores were feasible for all 995 children who self-reported age and gender. Total score did not differ between inside and outside environments(95% confidence interval(CI) of difference:-0.7 to 0.6;p = 0.91) or with/without footwear(95%CI of difference:-2.5 to 1.9; p = 0.77). Older age(p < 0.001, η2= 0.15) and male gender(p < 0.001, η2= 0.02)were associated with a higher total score. Inter-rater objectivity evidence was excellent(intraclass correlation coefficient(ICC) = 0.99) for completion time and substantial for skill score(ICC = 0.69) for 104 attempts by 53 children(34% female). Intra-rater objectivity was moderate(ICC = 0.52) for skill score and excellent for completion time(ICC = 0.99). Reliability was excellent for completion time over a short(2–4 days; ICC = 0.84) or long(8–14days; ICC = 0.82) interval. Skill score reliability was moderate(ICC = 0.46) over a short interval, and substantial(ICC = 0.74) over a long interval.Conclusion: The Canadian Agility and Movement Skill Assessment is a feasible measure of selected fundamental, complex and combined movement skills, which are an important building block for childhood physical literacy. Moderate-to-excellent objectivity was demonstrated for children 8–12 years of age. Test–retest reliability has been established over an interval of at least 1 week. The time and skill scores can be accurately estimated by 1 trained examiner.展开更多
Geiger considers dilettantism as an aesthetic attitude(inner concentration)that undermines the subject's ability to truly perceive the essential value of the artwork and thus calls for outer concentration to actua...Geiger considers dilettantism as an aesthetic attitude(inner concentration)that undermines the subject's ability to truly perceive the essential value of the artwork and thus calls for outer concentration to actualize the justice of the work itself.Geiger's position suggests that we must pay equitable attention to artworks and that this attitude requires a theory of aesthetic perception—phenomenological realism.However,the complexity of the aesthetic experience presents Geiger with several challenges,not least of which is the realisation of an attitude that bridges emotion and value in aesthetic experience.This paper will attempt to defend and develop the legitimacy of Geiger's position.First,I will clarify Geiger's critique of dilettantism and suggest how this critique distinguishes him from other aesthetic claims;second,I argue that Geiger argues for an aesthetic attitude that distinguishes everyday emotions and thus ensures the value of the work of art as its own;finally,I will complete Geiger's position by drawing on later phenomenology that answers the question of how we realise the perception of artworks.展开更多
Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this uniq...Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.展开更多
Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibilit...Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.展开更多
Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some ...BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.展开更多
Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of ...Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.展开更多
For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,whic...For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.展开更多
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 unman...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.展开更多
Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cam...Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.展开更多
文摘Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is grounded on a prepredicative,i. e. pre-linguistic and pre-communicative,level. I will endorse a Husserlian viewpoint on the issue,and I will try to develop some aspects of the Husserlian account of three-dimensional thing-perception by means of which I will showhowprepredicative experience can actually offer us a fundamental element of our common understanding of objectivity. In doing this,it will be necessary to acknowledge thing-perception as being primarily intertwined with indeterminacy. I will claim that only on the basis of such an intuitive and prepredicative access to the things as partially indeterminate,first,and as determinable,second,is it possible to have an understanding of the world as something (at least partially) independent from the intuition (s) all subjects can have of it. By means of the addition of a consciousness of the thing as accessible to other subjects,one achieves a vision of the thing as fully determinate in itself. This"vision",however,takes one to be aware of the determination of the thing as lying beyond any intuitive grasp of it. The result will,thus,be that the prepredicative constitution of our basic sense of objectivity leads us to intend the world as something which should be accounted for (also) by means of sources different from intuition.
文摘For a physically possible deformation field of a continuum, the deformation gradient function F can be decomposed into direct sum of a symmetric tensor S and on orthogonal tensor R, which is called S-R decomposition theorem. In this paper, the S-R decomposition unique existence theorem is proved, by employing matrix and tensor method. Also, a brief proof of its objectivity is given.
文摘This paper presents an analysis on how objectivity is discursively constructed in journalistic narratives by drawing on the theories of viewpoint and mental space in Cognitive Linguistics. It is posited that at least three mental spaces are projected by a narrative discourse, i.e., a narrated event space, a narrating space, and a basic space, and the distance between the first two spaces determines the degree of objectivity in the narrative discourse. A schema which represents the configuration of the different spaces is proposed and applied in the analysis of journalistic narratives to explore the strategies of objectivity construction. The analysis reveals that what the different journalistic narratives have in common in the construction of objectivity is to distance the narrated event space and the narrating space with the former being foregrounded in the viewpoint arrangement.
文摘In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Pauline Melville, we argue that Cartesianism has influenced a view on education which tends to consider good and valuable what is ＂scientific＂, ＂objective＂ and ＂universal＂. The subjective and the local seem to be considered undesirable and unreliable. Brazilian scholars on the education field, such as Coracini and Souza are important support for our argument.
文摘When scientific research began in early twentieth-century China,a key issue was the acquisition of reliable empirical information through objective and precise observations.This article examines a specific case where a scientist grappled with such an issue:the linguist Chao Yuen Ren’s application of mechanical means in his phonetic studies.In the 1920s–1930s,Chao conducted a series of field and lab studies on the dialects in southern and central China.In contrast to traditional scholars’exclusive reliance on sharp ears and rhyme books,Chao employed mechanical devices to inscribe and analyze the spectrographs of dialectical tones and used phonographs to record the articulations of his subjects.It is demonstrated that Chao’s machines not only provided a new method of observation;they also altered the theoretical understanding of certain fundamental categories in Chinese phonology,such as tones.Moreover,Chao did not aim to replace human perception with automatic mechanisms in empirical investigations.Rather,the use of machines in his research called for an active and engaged scientific persona.
文摘The world is facing a once-in-a-lifetime situation:the COVID-19 pandemic.During the pandemic,the World Health Organization announced an infodemic as well.This infodemic caused infollution and sparked many controversies.Pandemics as extraordinary occurrences are always attractive to historians.However,infodemics and biased information threaten objective history-writing.Objectivity as it regards historians is already a much-discussed subject.In this commentary,the fundamental theories about objectivity are delineated.Second,the relationship between the infodemic and COVID-19 pandemic is explained.Lastly,the problems regarding objectivity in the historiography of the COVID-19 pandemic are explored.
文摘China English, as one of the English varieties, is an objective reality. It is different from Chinglish which is an interlanguage for Chinese English learners. This paper expresses the definition of China English, its objectivity and manifestations in terms of pronunciation, vocabulary, syntax and text.
文摘The history of science and medicine has long been steeped in the notion that they are objective(untainted by the philosophical and ideological ebbs and flows of society)and utilitarian(doing what is best for the greater good).Because of this,scientific and medical epistemologies and praxis are often held to an esteem that is unquestioned,celebrated,and occasionally unchecked.A closer look at the history of science and medicine,however,readily reveal the extent to which the milieu of society has informed scientific and medical endeavors.As such,an understanding of how the subjectivities of scientific and medical endeavors situate within our contemporary disciplines and practices is significant to one’s ability to truly understand said disciplines.Likewise,such an evaluation will provide insight into our role in perpetuating the illusion of objectivity in these fields.With this in mind,this paper provides a philosophical and historical examination of the concept of objectivity(in contrast to subjectivity)in science and medicine.
基金funded by a grant from the Canadian Institutes of Health Research awarded to Dr. Meghann Lloyd and Dr. Mark Tremblay (IHD 94356)
文摘Purpose: The primary aim of this study was to develop an assessment of the fundamental, combined, and complex movement skills required to support childhood physical literacy. The secondary aim was to establish the feasibility, objectivity, and reliability evidence for the assessment.Methods: An expert advisory group recommended a course format for the assessment that would require children to complete a series of dynamic movement skills. Criterion-referenced skill performance and completion time were the recommended forms of evaluation. Children, 8–12 years of age, self-reported their age and gender and then completed the study assessments while attending local schools or day camps. Face validity was previously established through a Delphi expert(n = 19, 21% female) review process. Convergent validity was evaluated by age and gender associations with assessment performance. Inter-and intra-rater(n = 53, 34% female) objectivity and test–retest(n = 60, 47% female) reliability were assessed through repeated test administration.Results: Median total score was 21 of 28 points(range 5–28). Median completion time was 17 s. Total scores were feasible for all 995 children who self-reported age and gender. Total score did not differ between inside and outside environments(95% confidence interval(CI) of difference:-0.7 to 0.6;p = 0.91) or with/without footwear(95%CI of difference:-2.5 to 1.9; p = 0.77). Older age(p < 0.001, η2= 0.15) and male gender(p < 0.001, η2= 0.02)were associated with a higher total score. Inter-rater objectivity evidence was excellent(intraclass correlation coefficient(ICC) = 0.99) for completion time and substantial for skill score(ICC = 0.69) for 104 attempts by 53 children(34% female). Intra-rater objectivity was moderate(ICC = 0.52) for skill score and excellent for completion time(ICC = 0.99). Reliability was excellent for completion time over a short(2–4 days; ICC = 0.84) or long(8–14days; ICC = 0.82) interval. Skill score reliability was moderate(ICC = 0.46) over a short interval, and substantial(ICC = 0.74) over a long interval.Conclusion: The Canadian Agility and Movement Skill Assessment is a feasible measure of selected fundamental, complex and combined movement skills, which are an important building block for childhood physical literacy. Moderate-to-excellent objectivity was demonstrated for children 8–12 years of age. Test–retest reliability has been established over an interval of at least 1 week. The time and skill scores can be accurately estimated by 1 trained examiner.
文摘Geiger considers dilettantism as an aesthetic attitude(inner concentration)that undermines the subject's ability to truly perceive the essential value of the artwork and thus calls for outer concentration to actualize the justice of the work itself.Geiger's position suggests that we must pay equitable attention to artworks and that this attitude requires a theory of aesthetic perception—phenomenological realism.However,the complexity of the aesthetic experience presents Geiger with several challenges,not least of which is the realisation of an attitude that bridges emotion and value in aesthetic experience.This paper will attempt to defend and develop the legitimacy of Geiger's position.First,I will clarify Geiger's critique of dilettantism and suggest how this critique distinguishes him from other aesthetic claims;second,I argue that Geiger argues for an aesthetic attitude that distinguishes everyday emotions and thus ensures the value of the work of art as its own;finally,I will complete Geiger's position by drawing on later phenomenology that answers the question of how we realise the perception of artworks.
基金the National Natural Science Foundation of China(Grant No.52072041)the Beijing Natural Science Foundation(Grant No.JQ21007)+2 种基金the University of Chinese Academy of Sciences(Grant No.Y8540XX2D2)the Robotics Rhino-Bird Focused Research Project(No.2020-01-002)the Tencent Robotics X Laboratory.
文摘Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.
基金supported by a grant from the Basic Science Research Program through the National Research Foundation(NRF)(2021R1F1A1063634)funded by the Ministry of Science and ICT(MSIT),Republic of KoreaThe authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding Program Grant Code(NU/RG/SERC/13/40)+2 种基金Also,the authors are thankful to Prince Satam bin Abdulaziz University for supporting this study via funding from Prince Satam bin Abdulaziz University project number(PSAU/2024/R/1445)This work was also supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R54)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
基金The Shanxi Provincial Administration of Traditional Chinese Medicine,No.2023ZYYDA2005.
文摘BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.
基金supported in part by National Natural Science Foundation of China(No.62176041)in part by Excellent Science and Technique Talent Foundation of Dalian(No.2022RY21).
文摘Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.
基金Scientific Research Fund of Liaoning Provincial Education Department(No.JGLX2021030):Research on Vision-Based Intelligent Perception Technology for the Survival of Benthic Organisms.
文摘For underwater robots in the process of performing target detection tasks,the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model,which is prone to issues like error detection,omission detection,and poor accuracy.Therefore,this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7)underwater target detection algorithm.To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase,we have added a Convolutional Block Attention Module(CBAM)to the backbone network.The Reparameterization Visual Geometry Group(RepVGG)module is inserted into the backbone to improve the training and inference capabilities.The Efficient Intersection over Union(EIoU)loss is also used as the localization loss function,which reduces the error detection rate and missed detection rate of the algorithm.The experimental results of the CER-YOLOv7 algorithm on the UPRC(Underwater Robot Prototype Competition)dataset show that the mAP(mean Average Precision)score of the algorithm is 86.1%,which is a 2.2%improvement compared to the YOLOv7.The feasibility and validity of the CER-YOLOv7 are proved through ablation and comparison experiments,and it is more suitable for underwater target detection.
基金This research was funded by the Natural Science Foundation of Hebei Province(F2021506004).
文摘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.
基金supported by the Ningxia Key Research and Development Program(Talent Introduction Special Project)Project(2022YCZX0013)North Minzu University 2022 School-Level Scientific Research Platform“Digital Agriculture Enabling Ningxia Rural Revitalization Innovation Team”(2022PT_S10)+1 种基金Yinchuan City University-Enterprise Joint Innovation Project(2022XQZD009)Ningxia Key Research and Development Program(Key Project)Project(2023BDE02001).
文摘Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.
