期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到188篇文章
< 1 2 … 10 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Integrating Integration with Contextualization:A Study on Rural Teacher Education Curriculum Model Based on Henan Practice 认领 引用
1
作者 Na Duan Chenguang Zhang +2 位作者 Zhaoli Liu Jing Niu Xiaojie Yuan 《Journal of Contemporary Educational Research》 2026年第1期110-117,共8页
Against the backdrop of the strategy to build a strong education system,constructing a teacher education curriculum that meets the genuine needs of rural education has become a critical issue.In response to the existi... Against the backdrop of the strategy to build a strong education system,constructing a teacher education curriculum that meets the genuine needs of rural education has become a critical issue.In response to the existing dilemmas in current curricula-such as an“urban-centric”orientation,the disconnection between theory and practice,and the fragmentation between pre-service training and in-service professional development-this study proposes a reform concept centered on the integration of“Integration”and“Contextualization.”Using Henan Province as a practical field,the research constructs a systematic curriculum model.In the dimension of“Integration,”the model traverses the entire process of teacher professional development through mechanisms like the University-Government-School(U-G-S)collaboration,spiral curriculum modules,and a diversified evaluation system.In the dimension of“Contextualization,”it reshapes teachers’local sentiment and place-based practical abilities by integrating Central Plains culture and local knowledge into the curriculum content and innovating immersive practical teaching.This paper elaborates on the theoretical framework and the four key reconstruction dimensions of the model,and demonstrates its operability and effectiveness by focusing on typical cases such as the“Xinyang Maojian Tea Curriculum Development”and the“Xinxian County Small-Class Multi-Grade Teaching Training.”The model aims to provide a replicable and scalable practical solution to resolve the structural contradictions in rural teacher cultivation,holding demonstrative significance for promoting the high-quality development of regional rural teachers. 展开更多
关键词 Rural teacher education Curriculum model Integration Contextualization U-G-S collaboration Local literacy
暂未订购 下载PDF
Evaluation of the McGill‐Tongji Blended Education Program for Teacher Leaders in General Practice:The importance of partnership and contextualization in International Primary Care Training Initiatives 认领 引用
2
作者 Ziyue Wang Xinxin Zhao +14 位作者 Huixia Shen Hao Wang Gemma Cheng Ya Ning Gao Wenzhen Zuo Zhuyin Xu Francesco Avallone Anish KArora Manxi Guo Rachel Simmons David Lessard Theresa Beesley Jialin CZheng Bertrand Lebouché Howard Bergman 《Health Care Science》 2024年第4期238-248,共11页
Purpose:Strong primary health care(PHC)systems require well‐established PHC education systems to enhance the skills of general practitioners(GPs).However,the literature on the experiences of international collaborati... Purpose:Strong primary health care(PHC)systems require well‐established PHC education systems to enhance the skills of general practitioners(GPs).However,the literature on the experiences of international collaboration in primary care education in low‐and middle‐income countries remains limited.The purpose of this study was to evaluate the implementation and perceived impact of the McGill‐Tongji Blended Education Program for Teacher Leaders in General Practice(referred to as the“Tongji Program”).Methods:In 2020–2021,the McGill Department of Family Medicine(Montreal,Canada)and Tongji University School of Medicine(TUSM,Shanghai,China)jointly implemented the Tongji Program in Shanghai,China to improve the teaching capacity of PHC teachers.We conducted an exploratory longitudinal case study with a mixed methods design for the evaluation.Quantitative(QUAN)data was collected through questionnaire surveys and qualitative(QUAL)data was collected through focus group discussions.Results:The evaluation showed that learners in Tongji Program were primarily female GPs(21/22,95%)with less than 4 years of experience in teaching(16/22,73%).This program was considered a successful learning experience by most participants(19/22,86%)with higher order learning tasks such as critical thinking and problem‐solving.They also agreed that this program helped them feel more prepared to teach(21/22,95%),and developed a positive attitude toward primary care(21/22,95%).The QUAL interview revealed that both the Tongji and McGill organizers noted that TUSM showed strong leadership in organization,education,and coordination.Both students and teachers agreed that by adapting training content into contextualized delivery formats and settings,the Tongji Program successfully overcame language and technology barriers.Conclusions:Committed partnerships and contextualization were key to the success of the Tongji Program.Future research should focus on how international primary care education programs affect learners'behavior in their practice settings,and explore barriers and facilitators to change. 展开更多
关键词 primary care general practice continuing professional development contextualization international partnership
暂未订购 下载PDF
A Three-Dimensional Contextualization Established for an English Language-Learning-and-Teaching to Get Along With in the Classroom 认领 引用
3
作者 YAN Jing CHEN Si FEI Zi 《US-China Foreign Language》 2015年第5期333-340,共8页
If language is supposed to be the text, the question then comes to ＂what is the context within which language lives rather than die？＂. What context to language-learning is what water to plant-surviving. Questionabl... If language is supposed to be the text, the question then comes to ＂what is the context within which language lives rather than die？＂. What context to language-learning is what water to plant-surviving. Questionably, what is thought of previously as the context or linguistic environment, especially that for classroom situation, remains still a problem to be dealt with and, what this paper is trying to do is therefore to establish kind of a three-dimensional contextualization, which is hypothesized as being verbal and non-verbal, cultural and non-cultural as well as linguistic and non-linguistic for language teaching-and-learning to be carded out inside the classroom and, if possible, for English teachers to get along with. 展开更多
关键词 text verbal and non-verbal cultural and non-cultural linguistic and non-linguistic three-dimensional contextualization
暂未订购 下载PDF
Rigorous verification of quantum contextuality from anomalous weak value 认领 引用
4
作者 Wei-Qian Zhao Si-Nan Pang +3 位作者 Zi-Fu Su Tian-Ming Zhao Jin-Dong Wang Ya-Fei Yu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第2期306-313,共8页
Weak measurement offers a powerful framework for probing nonclassical features of quantum mechanics,with anomalous weak values serving as operational signatures of contextuality.While the anomalous weak value verifica... Weak measurement offers a powerful framework for probing nonclassical features of quantum mechanics,with anomalous weak values serving as operational signatures of contextuality.While the anomalous weak value verification of quantum contextuality has been predominantly investigated in the single-photon regime and analyzed under approximation condition of infinitesimally small perturbation strength.This study releases the approximation condition and takes into account the impact of perturbation strength on the rigor of the verification.And the investigation on the verification of contextuality is extended to the multi-photon scenarios for observing the influence of the correlation between photons on the verification.Without the limitation of infinitesimally small probability of disturbance,anomalous weak values are identified as necessary for contextuality to emerge,thereby refining the criterion proposed by Pusey[Phys.Rev.Lett.113200401(2014)].In the multi-photon scenarios,the emergence of contextuality also depends strongly on both the photon number and the photon-number distribution state.In particular,contextuality is found to be maximized when the single-photon component dominates and the second-order correlation is lower.These results highlight the critical role of photon statistics in experimental tests of contextuality via anomalous weak values. 展开更多
关键词 quantum measurement contextuality weak measurement weak values
暂未订购 下载PDF
Latent Space Iterative Weather Prediction:an Efficient Framework for Leveraging Rich Contextual Atmospheric Variables 认领 引用
5
作者 LI Shuangliang LI Siwei 《Journal of Geodesy and Geoinformation Science》 CSCD 2026年第1期11-49,共39页
Weather forecasting,which involves predicting a few critical atmospheric variables,is of significant importance to both scientific research and societal applications.Recently,deep learning methods have been introduced... Weather forecasting,which involves predicting a few critical atmospheric variables,is of significant importance to both scientific research and societal applications.Recently,deep learning methods have been introduced into this field due to their substantially reduced inference time and promising forecast accuracy.However,to comprehensively simulate atmospheric state evolution and improve the prediction accuracy of critical variables,most current approaches incorporate hundreds of auxiliary variables and iteratively predict all physical variables regardless of their relevance to critical variables,significantly increasing task complexity.Moreover,iterative forecasting of physical variables is susceptible to disturbances such as noise and missing values.To address these limitations,we propose a forecasting model to iteratively predict only the critical-variables-relevant atmospheric latent features rather than all physical variables,which achieves faster convergence and higher accuracy.These latent features are encoded and extracted from numerous variables,and they are guided by the prediction loss function to be relevant to critical atmospheric variables.Additionally,iteratively predicting latent features minimizes the impact of noise and missing values,as these are filtered out by the encoder,leading to more accurate and stable predictions.To balance performance and efficiency,we determine the optimal dimensionality of the latent features through theoretical analysis and ablation studies.Comprehensive experimental results on two ERA5 sub-datasets have demonstrated the effectiveness and efficiency of the proposed framework in improving forecasting accuracy. 展开更多
关键词 weather forecasting contextual atmospheric variables latent space iterative prediction
暂未订购 下载PDF
The Dorsal CA2 to Ventral CA1 Circuit Contributes to Spatial and Context-Dependent Memories 认领 引用
6
作者 Ning Li Bingqing Zhao +6 位作者 Xiaowen Shen Hua He Jianxiang Zhou Jing Zhang Lin Xu Xiaochun Chen Chun Xu 《Neuroscience Bulletin》 SCIE CAS CSCD 2026年第6期1235-1247,共13页
The hippocampal dorsal CA2 subregion(dCA2)is critical for social memory;however,its contribution to other types of hippocampus-dependent memories is not well understood.Here,we performed dCA2-specific circuit tracing,... The hippocampal dorsal CA2 subregion(dCA2)is critical for social memory;however,its contribution to other types of hippocampus-dependent memories is not well understood.Here,we performed dCA2-specific circuit tracing,single-neuron projectome analysis,photometric Ca2+imaging,and optogenetic manipulations to study the physiological roles of dCA2 neurons and their axon projections in behavioral paradigms for novel object recognition,novel location recognition,and contextual fear memory.We found that dCA2 neurons sent their strongest axon projections to the dorsal portion of ventral CA1(vCA1d)and showed object and location-specific Ca2+responses.Notably,the dCA2-vCA1d projection contributed to the memory formation of object location but not identity.Furthermore,optogenetic inhibition of the dCA2-vCA1d axon terminals reduced fear responses to foot shocks and impaired contextual memory formation.Collectively,our study reveals critical roles of the dCA2-vCA1d circuit in spatial and context-dependent memories,providing new insights into the function of CA2 neurons. 展开更多
关键词 Spatial memory Contextual memory Hippocampus CA2 Longitudinal circuit Single-neuron projectome Fiber photometry Optogenetics
暂未订购 下载PDF
Resolving Ambiguity in Pointing Gestures Using Contextual Reasoning from Large Language Models 认领 引用
7
作者 Sumin Yeon Minjae Lee +1 位作者 Jiho Bae Suwon Lee 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1010-1024,共15页
In everyday life,people effectively convey their intentions through pointing gestures without explicitly naming objects.In particular,pointing gestures used in conjunction with linguistic expressions such as“this”an... In everyday life,people effectively convey their intentions through pointing gestures without explicitly naming objects.In particular,pointing gestures used in conjunction with linguistic expressions such as“this”and“that”play a crucial role in intuitively indicating objects or locations in space.Although research on the recognition of such nonverbal gestures has been actively pursued within the field of human-computer interaction(HCI),accurately interpreting a user’s intent remains challenging in situations where the pointing gesture is ambiguous.This paper proposes an integrated system that combines a large language model(LLM),capable of understanding complex human language expressions,with pointing gestures designed to designate targets in space,thereby effectively processing multimodal user commands.The system is designed to accurately recognize user intentions even in complex and uncertain environments(e.g.,indoor spaces with multiple objects)by synergistically leveraging spatial information obtained from pointing gestures and contextual reasoning provided by the LLM.To validate the proposed approach,we constructed a dataset comprising complex real-world environments and diverse utterances,and conducted experiments to meticulously analyze the system’s performance and limitations.This study demonstrates the potential for natural expansion of language-based spatial understanding within HCI,and suggests avenues for future research in related fields. 展开更多
关键词 Multimodal interaction pointing gesture large language model contextual reasoning object referencing human-computer interaction
暂未订购 下载PDF
A Multi-Branch Transformer-Enhanced Neural Framework for Joint Morphological Representation Learning 认领 引用
8
作者 Laura Baitenova Gulnar Mukhamejanova +2 位作者 Gauhar Munaitbas Saken Mambetov Zhanna Mukanova 《Computers, Materials & Continua》 SCIE EI 2026年第9期1516-1540,共25页
Morphological parsing is a fundamental task in natural language processing,particularly for morphologically rich languages where words encode complex grammatical and semantic information.This paper proposes a multi-br... Morphological parsing is a fundamental task in natural language processing,particularly for morphologically rich languages where words encode complex grammatical and semantic information.This paper proposes a multi-branch Transformer-enhanced neural framework for joint morphological representation learning,designed to improve segmentation and classification accuracy by integrating complementary feature extraction mechanisms.The proposed architecture combines convolutional layers for capturing local morphological patterns,recurrent layers for modeling sequential dependencies,and Transformer-based self-attention for learning global contextual relationships.This hybrid design enables the model to generate robust and context-aware representations that enhance morphological understanding.The framework is trained using morphologically annotated datasets and evaluated using standard performance metrics,including F1-score and classification accuracy.Experimental results demonstrate that the proposed model significantly outperforms conventional and single-architecture baselines in both segmentation and morphological classification tasks.The learned representations exhibit strong discriminative capability,allowing accurate identification of morpheme boundaries and grammatical features.Furthermore,the model demonstrates stable convergence behavior and strong generalization performance across diverse linguistic conditions.These findings confirm the effectiveness of integrating multi-level contextual and structural feature extraction mechanisms,establishing the proposed framework as a robust and scalable solution for advanced morphological parsing and representation learning in modern natural language processing applications. 展开更多
关键词 Morphological parsing transformer architecture multi-branch neural networks morphological segmentation contextual representation learning natural language processing morphological classification deep learning self-attention mechanism
暂未订购 下载PDF
SI-FACT:mitigating knowledge conflict via self-improving faithfulness-aware contrastive tuning 认领 引用
9
作者 Fu Shengqiang Chen Shudong +3 位作者 Chen Xiaoxiao Yu Yong Du Jiale Wang Ergang 《High Technology Letters》 EI CAS 2026年第2期156-168,共13页
Large language models(LLMs)often produce unfaithful responses in knowledge-intensive tasks due to knowledge conflict—a tendency to rely on internal memory rather than user-provided context.This work aims to enhance t... Large language models(LLMs)often produce unfaithful responses in knowledge-intensive tasks due to knowledge conflict—a tendency to rely on internal memory rather than user-provided context.This work aims to enhance the model's trust in user-provided contextual information,encouraging it to ground its generation more faithfully on external evidence.To address this issue,we propose selfimproving faithfulness-aware contrastive tuning(SI-FACT),a novel self-improving framework that enhances contextual faithfulness while reducing data dependence.SI-FACT employs a self-instruct mechanism that enables the base LLM to automatically generate structured contrastive data consisting of anchor,faithful(positive),and unfaithful(negative)samples,thus eliminating costly manual annotation.Through contrastive learning,the model learns to align faithful responses closer and separate unfaithful ones within the representation space,effectively reinforcing trust in contextual evidence.Experiments on ECARE_KRE and COSE_KRE benchmarks show that SI-FACT,built on Llama-3-8B-Instruct,improves the contextual consistency rate(CCR)by 6.2%over the best baseline,while using fewer samples.These results demonstrate that SI-FACT achieves strong contextual faithfulness with high data efficiency,offering a practical path toward more reliable and proactive LLMs. 展开更多
关键词 knowledge conflict contrastive learning self-instruct large language models contextual faithfulness
暂未订购 下载PDF
A Dynamic Masking-Based Multi-Learning Framework for Sparse Classification 认领 引用
10
作者 Woo Hyun Park Dong Ryeol Shin 《Computers, Materials & Continua》 SCIE EI 2026年第3期1365-1380,共16页
With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study p... With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study proposes a new model,the Masked Joint Representation Model(MJRM).MJRM approximates the original hypothesis by leveraging multiple elements in a limited context.It dynamically adapts to changes in characteristics based on data distribution through three main components.First,masking-based representation learning,termed selective dynamic masking,integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets,whose predictions are then aggregated with optimized weights.This design alleviates sparsity,suppresses noise,and preserves contextual structures.Second,regularization-based improvements are applied.Third,techniques for addressing sparse data are used to perform final inference.As a result,MJRM improves performance by up to 4%compared to existing AI techniques.In our experiments,we analyzed the contribution of each factor,demonstrating that masking,dynamic learning,and aggregating multiple instances complement each other to improve performance.This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification,and can be useful even in challenging situations such as data shortage or data distribution variations.We expect that the approach can be extended to diverse fields such as sentiment analysis,spam filtering,and domain-specific document classification. 展开更多
关键词 Text classification dynamic learning contextual features data sparsity masking-based representation
暂未订购 下载PDF
Validation of Contextual Model Principles through Rotated Images Interpretation 认领 引用
11
作者 Illia Khurtin Mukesh Prasad 《Computers, Materials & Continua》 SCIE EI 2026年第2期535-549,共15页
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
关键词 Visual information processing spatial transformations recognition contextual model context
暂未订购 下载PDF
MMHCA:Multi-feature representations based on multi-scale hierarchical contextual aggregation for UAV-view geo-localization 认领 引用 被引量：3
12
作者 Nanhua CHEN Tai-shan LOU Liangyu ZHAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第6期517-532,共16页
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e... In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation. 展开更多
关键词 Geo-localization Image retrieval UAV Hierarchical contextual aggregation Multi-feature representations
暂未订购 下载PDF
Flex-QUIC:AI for QUIC Transport Protocol with High-Efficiency in Future Wireless Networks 认领 引用 被引量：1
13
作者 Jiang Tao Liu Yang +2 位作者 Zhang Yu Peng Miaoran Wang Haoyu 《China Communications》 SCIE EI CSCD 2025年第12期1-14,共14页
This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowle... This paper proposes Flex-QUIC,an AIempowered quick UDP Internet connections(QUIC)enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment(ACK)mechanisms,loss detection,and forward error correction(FEC)in dynamic wireless networks.Unlike the standard QUIC protocol,Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation.First,a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention.Second,the adaptive loss detection module utilizes a long short-term memory(LSTM)model to predict the reordering displacement for optimizing the packet reordering tolerance.Third,the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state.Extensive evaluations across Wi-Fi,5G,and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants,highlighting its adaptability to diverse and dynamic network conditions.Finally,we further discuss open issues in deploying AI-native transport protocols. 展开更多
关键词 contextual multi-armed bandit long shortterm memory QUIC transport protocol wireless networks
暂未订购 下载PDF
CPEWS:Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation 认领 引用 被引量：1
14
作者 Xiaoyan Shao Jiaqi Han +2 位作者 Lingling Li Xuezhuan Zhao Jingjing Yan 《Computers, Materials & Continua》 SCIE EI 2025年第4期595-617,共23页
The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gaine... The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods.End-to-end model designs have gained significant attention for improving training efficiency.Most current algorithms rely on Convolutional Neural Networks(CNNs)for feature extraction.Although CNNs are proficient at capturing local features,they often struggle with global context,leading to incomplete and false Class Activation Mapping(CAM).To address these limitations,this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation(CPEWS)model,which improves feature extraction by utilizing the Vision Transformer(ViT).By incorporating its intermediate feature layers to preserve semantic information,this work introduces the Intermediate Supervised Module(ISM)to supervise the final layer’s output,reducing boundary ambiguity and mitigating issues related to incomplete activation.Additionally,the Contextual Prototype Module(CPM)generates class-specific prototypes,while the proposed Prototype Discrimination Loss and Superclass Suppression Loss guide the network’s training,(LPDL)(LSSL)effectively addressing false activation without the need for extra supervision.The CPEWS model proposed in this paper achieves state-of-the-art performance in end-to-end weakly supervised semantic segmentation without additional supervision.The validation set and test set Mean Intersection over Union(MIoU)of PASCAL VOC 2012 dataset achieved 69.8%and 72.6%,respectively.Compared with ToCo(pre trained weight ImageNet-1k),MIoU on the test set is 2.1%higher.In addition,MIoU reached 41.4%on the validation set of the MS COCO 2014 dataset. 展开更多
关键词 End-to-end weakly supervised semantic segmentation vision transformer contextual prototype class activation map
暂未订购 下载PDF
Mobility-Aware User Scheduling in Wireless Federated Learning with Contextual Multi-Armed Bandit 认领 引用 被引量：1
15
作者 Li Jun Sun Haiyang +4 位作者 Deng Xiumei Wei Kang Shi Long Liang Le Chen Wen 《China Communications》 SCIE EI CSCD 2025年第11期256-272,共17页
Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performa... Federated learning(FL)is an intricate and privacy-preserving technique that enables distributed mobile devices to collaboratively train a machine learning model.However,in real-world FL scenarios,the training performance is affected by a combination of factors such as the mobility of user devices,limited communication and computational resources,thus making the user scheduling problem crucial.To tackle this problem,we jointly consider the user mobility,communication and computational capacities,and develop a stochastic optimization problem to minimize the convergence time.Specifically,we first establish a convergence bound on the training performance based on the heterogeneity of users’data,and then leverage this bound to derive the participation rate for each user.After deriving the user-specific participation rate,we aim to minimize the training latency by optimizing user scheduling under the constraints of the energy consumption and participation rate.Afterward,we transform this optimization problem to the contextual multi-armed bandit framework based on the Lyapunov method and solve it with the submodular reward enhanced linear upper confidence bound(SR-linUCB)algorithm.Experimental results demonstrate the superiority of our proposed algorithm on the training performance and time consumption compared with stateof-the-art algorithms for both independent and identically distributed(IID)and non-IID settings. 展开更多
关键词 contextual multi-armed bandit federated learning resource allocation upper confidence bound user scheduling
暂未订购 下载PDF
Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells 认领 引用
16
作者 Chuanyun Xu Die Hu +3 位作者 Yang Zhang Shuaiye Huang Yisha Sun Gang Li 《Computers, Materials & Continua》 SCIE EI 2025年第4期559-574,共16页
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
关键词 Cervical abnormal cells image detection multi-scale feature fusion contextual information
暂未订购 下载PDF
Multi-Stage Vision Transformer and Knowledge Graph Fusion for Enhanced Plant Disease Classification 认领 引用
17
作者 Wafaa H.Alwan Sabah M.Alturfi 《Computer Systems Science & Engineering》 2025年第1期419-434,共16页
Plant diseases pose a significant challenge to global agricultural productivity,necessitating efficient and precise diagnostic systems for early intervention and mitigation.In this study,we propose a novel hybrid fram... Plant diseases pose a significant challenge to global agricultural productivity,necessitating efficient and precise diagnostic systems for early intervention and mitigation.In this study,we propose a novel hybrid framework that integrates EfficientNet-B8,Vision Transformer(ViT),and Knowledge Graph Fusion(KGF)to enhance plant disease classification across 38 distinct disease categories.The proposed framework leverages deep learning and semantic enrichment to improve classification accuracy and interpretability.EfficientNet-B8,a convolutional neural network(CNN)with optimized depth and width scaling,captures fine-grained spatial details in high-resolution plant images,aiding in the detection of subtle disease symptoms.In parallel,ViT,a transformer-based architecture,effectively models long-range dependencies and global structural patterns within the images,ensuring robust disease pattern recognition.Furthermore,KGF incorporates domain-specific metadata,such as crop type,environmental conditions,and disease relationships,to provide contextual intelligence and improve classification accuracy.The proposed model was rigorously evaluated on a large-scale dataset containing diverse plant disease images,achieving outstanding performance with a 99.7%training accuracy and 99.3%testing accuracy.The precision and F1-score were consistently high across all disease classes,demonstrating the framework’s ability to minimize false positives and false negatives.Compared to conventional deep learning approaches,this hybrid method offers a more comprehensive and interpretable solution by integrating self-attention mechanisms and domain knowledge.Beyond its superior classification performance,this model opens avenues for optimizing metadata dependency and reducing computational complexity,making it more feasible for real-world deployment in resource-constrained agricultural settings.The proposed framework represents an advancement in precision agriculture,providing scalable,intelligent disease diagnosis that enhances crop protection and food security. 展开更多
关键词 Plant disease classification EfficientNet-B8 vision transformer knowledge graph fusion precision agriculture deep learning contextual metadata
暂未订购 下载PDF
Chinese Translation of Japanese Quotation Sentences From the Perspective of Contextual Adaptation 认领 引用
18
作者 DING Pinyue 《Sino-US English Teaching》 2025年第2期48-52,共5页
Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One f... Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication. 展开更多
关键词 contextual adaptation communicative context direct quotation
暂未订购 下载PDF
An Action Research on Music Appreciation Activities in Kindergarten Classes Based on Situational Teaching Approach 认领 引用
19
作者 Jin Wu 《Journal of Contemporary Educational Research》 2025年第3期94-101,共8页
Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently... Abs As a crucial vehicle for young children’s artistic enlightenment,music appreciation holds an irreplaceable value in cognitive development,emotional edification,and the cultivation of aesthetic abilities.Currently,in music appreciation activities for senior kindergarten classes,there is a widespread phenomenon of homogenized teaching content and mechanized teaching methods,which results in insufficient enthusiasm for participation among young children and a superficial understanding of music.The situational teaching method,by constructing concrete and immersive learning scenarios,can effectively activate young children’s multi-dimensional sensory experiences.Its characteristics of intuitiveness and interactivity are highly consistent with the traits of young children’s concrete thinking,thus providing a new approach to resolving the current predicament.The research focuses on the practical pain points in music appreciation activities for senior kindergarten classes and proposes targeted solutions from four dimensions:content design,method innovation,resource integration,and teacher training,aiming to reconstruct a child-centered,in-depth music learning model.Practice has shown that the situational teaching method can not only enhance young children’s perceptual sensitivity to musical elements but also guide them to achieve emotional resonance through role-playing and life-related associations,laying a foundation for the sustainable development of young children’s musical literacy. 展开更多
关键词 Contextual pedagogy Kindergarten class Music appreciation activities Problems and measures
暂未订购 下载PDF
SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission 认领 引用 被引量：8
20
作者 Huanrong Tang Aoming Peng +2 位作者 Dongming Zhang Tianming Liu Jianquan Ouyang 《Computers, Materials & Continua》 SCIE EI 2020年第1期293-307,共15页
With the improvement of the national economic level,the number of vehicles is still increasing year by year.According to the statistics of National Bureau of Statics,the number is approximately up to 327 million in Ch... With the improvement of the national economic level,the number of vehicles is still increasing year by year.According to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing.Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much manpower.Due to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and precision.Thus,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified layer.Finally,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training cost.Meanwhile,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking. 展开更多
关键词 Contextual information transmission illegal parking detection spatial attention mechanism deep learning
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 10 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈