Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insigh...Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effective MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models’generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.展开更多
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
While the increasing development of modern information technology, the globalization is becoming an obvious feature on educational situations. Therefore, mastering some necessary bilingual competencies will present es...While the increasing development of modern information technology, the globalization is becoming an obvious feature on educational situations. Therefore, mastering some necessary bilingual competencies will present essential meaning for educators. In the case of language teachers who teaching in the ethnically plural countries, for instance, China, the United States, language teachers have to face to various difficulties on the process of teaching in the bilingual class. Currently, the advanced technology is gradually being applied into language teaching, and then provides a series of advantages on improving the quality of language teaching. Firstly, the essay will analyse the barriers which exist in the language class, which in the level of Chinese university. Secondly, it will systematically describe how ICT can help language teachers to solve difficulties on teaching and display diverse innovative technological tools of language teaching.展开更多
With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.He...With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.展开更多
Finding out out-of-vocabulary words is an urgent and difficult task in Chinese words segmentation. To avoid the defect causing by offline training in the traditional method, the paper proposes an improved prediction b...Finding out out-of-vocabulary words is an urgent and difficult task in Chinese words segmentation. To avoid the defect causing by offline training in the traditional method, the paper proposes an improved prediction by partical match （PPM） segmenting algorithm for Chinese words based on extracting local context information, which adds the context information of the testing text into the local PPM statistical model so as to guide the detection of new words. The algorithm focuses on the process of online segmentatien and new word detection which achieves a good effect in the close or opening test, and outperforms some well-known Chinese segmentation system to a certain extent.展开更多
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
Ontology-based semantic information integration resolve the schema-level heterogeneity and part of data level heterogeneity between distributed data sources. But it is ubiquitous that schema semantics of information i...Ontology-based semantic information integration resolve the schema-level heterogeneity and part of data level heterogeneity between distributed data sources. But it is ubiquitous that schema semantics of information is identical while the interpretation of it varies with different context, and ontology-based semantic information integration can not resolve this context heterogeneity. By introducing context representation and context mediation to ontology based information integration, the attribute-level context heterogeneity can be detected and reconciled automatically, and hence a complete solution for semantic heterogeneity is formed. Through a concrete example, the context representation and the process in which the attribute-level context heterogeneity is reconciled during query processing are presented. This resolution can make up the deficiency of schema mapping based semantic information integration. With the architecture proposed in this paper the semantic heterogeneity solution is adaptive and extensive.展开更多
With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision su...With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.展开更多
Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variati...Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.展开更多
The notion of context provides flexibility and adaptation to cloud computing services. Location, time identity and activity of users are examples of primary context types. The motivation of this paper is to formalize ...The notion of context provides flexibility and adaptation to cloud computing services. Location, time identity and activity of users are examples of primary context types. The motivation of this paper is to formalize reasoning about context information in cloud computing environments. To formalize such context-aware reasoning, the logic LCM of context-mixture is introduced based on a Gentzen-type sequent calculus for an extended resource-sensitive logic. LCM has a specific inference rule called the context-mixture rule, which can naturally represent a mechanism for merging formulas with context information. Moreover, LCM has a specific modal operator called the sequence modal operator, which can suitably represent context information. The cut-elimination and embedding theorems for LCM are proved, and a fragment of LCM is shown to be decidable. These theoretical results are intended to provide a logical justification of context-aware cloud computing service models such as a flowable service model.展开更多
The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issu...The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.展开更多
Accurate valve nozzle detection is an important component of industrial visual inspection systems;however,structural complexity,scale variation,illumination fluctuation,and partial occlusion remain challenging factors...Accurate valve nozzle detection is an important component of industrial visual inspection systems;however,structural complexity,scale variation,illumination fluctuation,and partial occlusion remain challenging factors that affect detection stability.This study presents YOLOv12-Enhanced,a refined singlestage detection framework developed for industrial valve nozzle scenarios.The proposed approach incorporates three architectural modifications:a RepViT backbone to enhance hierarchical feature representation through structural re-parameterization and global–local modeling,a Spatial Pyramid Pooling Fast(SPPF)module combined with C2PSA attention to strengthen multi-scale contextual feature extraction,and a Global Edge Information Fusion(GEIF)module to integrate shallow edge information with deep semantic features for improved boundary alignment.Experimental evaluation on the Pascal Visual Object Classes(VOC)dataset shows that the proposed model achieves 71.0%mAP50 and 54.4%mAP50–95 under identical training conditions,exceeding the baseline YOLOv12n.Ablation experiments further demonstrate that each module contributes incremental performance gains.Evaluation on a self-constructed valve nozzle dataset consisting of 500 real industrial images indicates stable detection behavior under varying illumination and partial occlusion conditions.The experimental findings suggest that the proposed structural refinements provide a balanced enhancement in feature representation and localization precision while maintaining comparable computational complexity.展开更多
A context-aware privacy protection framework was designed for context-aware services and privacy control methods about access personal information in pervasive environment. In the process of user＇s privacy decision, ...A context-aware privacy protection framework was designed for context-aware services and privacy control methods about access personal information in pervasive environment. In the process of user＇s privacy decision, it can produce fuzzy privacy decision as the change of personal information sensitivity and personal information receiver trust. The uncertain privacy decision model was proposed about personal information disclosure based on the change of personal information receiver trust and personal information sensitivity. A fuzzy privacy decision information system was designed according to this model. Personal privacy control policies can be extracted from this information system by using rough set theory. It also solves the problem about learning privacy control policies of personal information disclosure.展开更多
According to Edward Hall,cultures can be categorized as being either high or low context.Chinese culture is typically high-context culture.The paper tries to explore the mode of information transfer in high-context cu...According to Edward Hall,cultures can be categorized as being either high or low context.Chinese culture is typically high-context culture.The paper tries to explore the mode of information transfer in high-context cultures in terms of Red Cliff.The discussion will be focused on dialogues and the costume.This will help understand the characteristics of high-context culture and make the communication between people from cultures of different contexts easier.展开更多
Aiming at the problems such as diverse target scales and large-scale changes in crowds in dense crowd scenarios,a crowd density estimation method based on multi-scale feature fusion and information en-hancement is pro...Aiming at the problems such as diverse target scales and large-scale changes in crowds in dense crowd scenarios,a crowd density estimation method based on multi-scale feature fusion and information en-hancement is proposed.Firstly,considering that small-scale targets account for a relatively large proportion in the image,based on the VGG-16 network,the dilated convolution module is introduced to mine the detailed information of the image.Secondly,in order to make full use of the multi-scale information of the target,a new context-aware module is constructed to extract the contrast features between different scales.Finally,con-sidering the characteristic of continuous changes in the target scale,a multi-scale feature aggregation module is designed to enhance the sampling range of dense scales and multi-scale information interaction,thereby improving the network performance.Experiments on public datasets show that the proposed method in this paper can effectively estimate the population density compared with other advanced methods.展开更多
基金supported by the Natural Science Foundation of Wenzhou University of Technology,China(Grant No.:ky202211).
文摘Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effective MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models’generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
文摘While the increasing development of modern information technology, the globalization is becoming an obvious feature on educational situations. Therefore, mastering some necessary bilingual competencies will present essential meaning for educators. In the case of language teachers who teaching in the ethnically plural countries, for instance, China, the United States, language teachers have to face to various difficulties on the process of teaching in the bilingual class. Currently, the advanced technology is gradually being applied into language teaching, and then provides a series of advantages on improving the quality of language teaching. Firstly, the essay will analyse the barriers which exist in the language class, which in the level of Chinese university. Secondly, it will systematically describe how ICT can help language teachers to solve difficulties on teaching and display diverse innovative technological tools of language teaching.
文摘With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.
基金National Natural Science Foundation of China ( No.60903129)National High Technology Research and Development Program of China (No.2006AA010107, No.2006AA010108)Foundation of Fujian Province of China (No.2008F3105)
文摘Finding out out-of-vocabulary words is an urgent and difficult task in Chinese words segmentation. To avoid the defect causing by offline training in the traditional method, the paper proposes an improved prediction by partical match （PPM） segmenting algorithm for Chinese words based on extracting local context information, which adds the context information of the testing text into the local PPM statistical model so as to guide the detection of new words. The algorithm focuses on the process of online segmentatien and new word detection which achieves a good effect in the close or opening test, and outperforms some well-known Chinese segmentation system to a certain extent.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
基金The National Natural Science Foundation of China (No.50305007)the Scientific Research Project of Hubei Provincial Department of Education (No.D200618003)
文摘Ontology-based semantic information integration resolve the schema-level heterogeneity and part of data level heterogeneity between distributed data sources. But it is ubiquitous that schema semantics of information is identical while the interpretation of it varies with different context, and ontology-based semantic information integration can not resolve this context heterogeneity. By introducing context representation and context mediation to ontology based information integration, the attribute-level context heterogeneity can be detected and reconciled automatically, and hence a complete solution for semantic heterogeneity is formed. Through a concrete example, the context representation and the process in which the attribute-level context heterogeneity is reconciled during query processing are presented. This resolution can make up the deficiency of schema mapping based semantic information integration. With the architecture proposed in this paper the semantic heterogeneity solution is adaptive and extensive.
文摘With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.
基金the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2023GXJS163,ZDYF2024GXJS014)National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)+2 种基金the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012)Hainan Provincial Natural Science Foundation of China(Grant No.620MS021)Youth Foundation Project of Hainan Natural Science Foundation(621QN211).
文摘Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.
文摘The notion of context provides flexibility and adaptation to cloud computing services. Location, time identity and activity of users are examples of primary context types. The motivation of this paper is to formalize reasoning about context information in cloud computing environments. To formalize such context-aware reasoning, the logic LCM of context-mixture is introduced based on a Gentzen-type sequent calculus for an extended resource-sensitive logic. LCM has a specific inference rule called the context-mixture rule, which can naturally represent a mechanism for merging formulas with context information. Moreover, LCM has a specific modal operator called the sequence modal operator, which can suitably represent context information. The cut-elimination and embedding theorems for LCM are proved, and a fragment of LCM is shown to be decidable. These theoretical results are intended to provide a logical justification of context-aware cloud computing service models such as a flowable service model.
文摘The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.
文摘Accurate valve nozzle detection is an important component of industrial visual inspection systems;however,structural complexity,scale variation,illumination fluctuation,and partial occlusion remain challenging factors that affect detection stability.This study presents YOLOv12-Enhanced,a refined singlestage detection framework developed for industrial valve nozzle scenarios.The proposed approach incorporates three architectural modifications:a RepViT backbone to enhance hierarchical feature representation through structural re-parameterization and global–local modeling,a Spatial Pyramid Pooling Fast(SPPF)module combined with C2PSA attention to strengthen multi-scale contextual feature extraction,and a Global Edge Information Fusion(GEIF)module to integrate shallow edge information with deep semantic features for improved boundary alignment.Experimental evaluation on the Pascal Visual Object Classes(VOC)dataset shows that the proposed model achieves 71.0%mAP50 and 54.4%mAP50–95 under identical training conditions,exceeding the baseline YOLOv12n.Ablation experiments further demonstrate that each module contributes incremental performance gains.Evaluation on a self-constructed valve nozzle dataset consisting of 500 real industrial images indicates stable detection behavior under varying illumination and partial occlusion conditions.The experimental findings suggest that the proposed structural refinements provide a balanced enhancement in feature representation and localization precision while maintaining comparable computational complexity.
基金Supported by the National Natural Science Foundation of China (60573119, 604973098) and IBM joint project
文摘A context-aware privacy protection framework was designed for context-aware services and privacy control methods about access personal information in pervasive environment. In the process of user＇s privacy decision, it can produce fuzzy privacy decision as the change of personal information sensitivity and personal information receiver trust. The uncertain privacy decision model was proposed about personal information disclosure based on the change of personal information receiver trust and personal information sensitivity. A fuzzy privacy decision information system was designed according to this model. Personal privacy control policies can be extracted from this information system by using rough set theory. It also solves the problem about learning privacy control policies of personal information disclosure.
文摘According to Edward Hall,cultures can be categorized as being either high or low context.Chinese culture is typically high-context culture.The paper tries to explore the mode of information transfer in high-context cultures in terms of Red Cliff.The discussion will be focused on dialogues and the costume.This will help understand the characteristics of high-context culture and make the communication between people from cultures of different contexts easier.
文摘Aiming at the problems such as diverse target scales and large-scale changes in crowds in dense crowd scenarios,a crowd density estimation method based on multi-scale feature fusion and information en-hancement is proposed.Firstly,considering that small-scale targets account for a relatively large proportion in the image,based on the VGG-16 network,the dilated convolution module is introduced to mine the detailed information of the image.Secondly,in order to make full use of the multi-scale information of the target,a new context-aware module is constructed to extract the contrast features between different scales.Finally,con-sidering the characteristic of continuous changes in the target scale,a multi-scale feature aggregation module is designed to enhance the sampling range of dense scales and multi-scale information interaction,thereby improving the network performance.Experiments on public datasets show that the proposed method in this paper can effectively estimate the population density compared with other advanced methods.
基金SuppoSed by the National Natural Science Foundation of China under Grant Nos.6067319560703078(国家自然科学基金)+2 种基金the National High-Tech Research and Development Plan of China under Grant No.2007AA04Z113(国家高技术研究发展计划(863))the National Basic Research Program of China under Grant No.2006CB303105(国家重点基础研究发展规划(973))the National Key Technology R&D Program of China under Grant No.2006BAF01A17(国家科技支撑计划)
