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
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds...In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.展开更多
Objectivity is the name given to covariance with respect to changes in observers by the action of the Euclidean group and is a central concept for mathematical modelling in Physics and Continuum mechanics.However,this...Objectivity is the name given to covariance with respect to changes in observers by the action of the Euclidean group and is a central concept for mathematical modelling in Physics and Continuum mechanics.However,this notion is delicate and therefore hard to understand by both mechanicians and mathematicians,precisely because it resides at the junction of the two disciplines and hence appeals to knowledge and notions from both fields to become clear.In a first part(“behind objectivity”)some classical notions of objectivity and frame-dependence are presented,discussed and critically revisited with the purpose to introduce in a second part(“beyond objectivity”)a novel modelling approach in incompatible elasticity,based on intrinsic and geometric concepts,related but distinct from conventional objectivity.This contribution is primarily conceived to assist students,academics and researchers,mathematicians in particular,to find their way through some difficult and often ill-understood concepts of Physics and Continuum mechanics,since objectivity still remains nowadays a profound concept that deserves our attention.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.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Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
文摘This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
基金funded by the Joint Funds of the National Natural Science Foundation of China(U2341223)the Beijing Municipal Natural Science Foundation(No.4232067).
文摘In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.
基金funded by national funds through FCT-Foundation for Science and Technology,project reference:UIDB/04561/2020
文摘Objectivity is the name given to covariance with respect to changes in observers by the action of the Euclidean group and is a central concept for mathematical modelling in Physics and Continuum mechanics.However,this notion is delicate and therefore hard to understand by both mechanicians and mathematicians,precisely because it resides at the junction of the two disciplines and hence appeals to knowledge and notions from both fields to become clear.In a first part(“behind objectivity”)some classical notions of objectivity and frame-dependence are presented,discussed and critically revisited with the purpose to introduce in a second part(“beyond objectivity”)a novel modelling approach in incompatible elasticity,based on intrinsic and geometric concepts,related but distinct from conventional objectivity.This contribution is primarily conceived to assist students,academics and researchers,mathematicians in particular,to find their way through some difficult and often ill-understood concepts of Physics and Continuum mechanics,since objectivity still remains nowadays a profound concept that deserves our attention.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
