End-to-end object detection Transformer(DETR)successfully established the paradigm of the Transformer architecture in the field of object detection.Its end-to-end detection process and the idea of set prediction have ...End-to-end object detection Transformer(DETR)successfully established the paradigm of the Transformer architecture in the field of object detection.Its end-to-end detection process and the idea of set prediction have become one of the hottest network architectures in recent years.There has been an abundance of work improving upon DETR.However,DETR and its variants require a substantial amount of memory resources and computational costs,and the vast number of parameters in these networks is unfavorable for model deployment.To address this issue,a greedy pruning(GP)algorithm is proposed,applied to a variant denoising-DETR(DN-DETR),which can eliminate redundant parameters in the Transformer architecture of DN-DETR.Considering the different roles of the multi-head attention(MHA)module and the feed-forward network(FFN)module in the Transformer architecture,a modular greedy pruning(MGP)algorithm is proposed.This algorithm separates the two modules and applies their respective optimal strategies and parameters.The effectiveness of the proposed algorithm is validated on the COCO 2017 dataset.The model obtained through the MGP algorithm reduces the parameters by 49%and the number of floating point operations(FLOPs)by 44%compared to the Transformer architecture of DN-DETR.At the same time,the mean average precision(mAP)of the model increases from 44.1%to 45.3%.展开更多
Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entere...Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entered the public eye,marking a significant milestone in LLM advancement.展开更多
Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artific...Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.展开更多
Social transformations have been changed in thinking and observable current religious institutions in contemporary architectural manifestations. Likewise, observed these changes in religious buildings of the past, the...Social transformations have been changed in thinking and observable current religious institutions in contemporary architectural manifestations. Likewise, observed these changes in religious buildings of the past, they were current and contemporary in its own time, which has allowed them to remain in force in its architectural spatiality and its urban relationships, serving as the historical bridge 1;hat allows analyzing the contemporary perspective in Mexico and particularly in Yucatan. The goal of this paper is to carry out a contemporary look to the interior and exterior of the temples in Yucatan colonial architecture to establish the process of change and permanence of spatiality and volumes that have allowed liturgy procedures as well as the way of approach to the sacredness of the Catholic communities from the colonial period to the present day. On the other hand, interest to establish, in particular, as the colonials religious buildings have been maintained in force and have appreciated for religious service to continue and in some cases, as they have given other kinds of services while maintaining its primordial spatiality.展开更多
Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease...Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .展开更多
Architecture transformations are frequently performed during software design and maiatenance. However this activity is not well supported at a sufficiently abstract level. In this paper, the authors characterize archi...Architecture transformations are frequently performed during software design and maiatenance. However this activity is not well supported at a sufficiently abstract level. In this paper, the authors characterize architecture transformations using graph rewriting rules, where architectures are represented in graph notations. Architectures are usually required to satisfy certain constraints during evolution. Therefore a way is presented to construct the suffi- cient and necessary condition for a transformation to preserve a constraint. The condition can be verified before the application of the transformation. Validated transformations are guaranteed not to violate corresponding constraints whenever applied.展开更多
This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business v...This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.展开更多
The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehe...The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehensive review of Transformer-based arrhythmia classification methods,examining their evolution,current capabilities,and future potential.We systematically analyze the architectural adaptations of Transformers for ECG signal processing,including Vision Transformers adapted for 1D medical signals,hybrid CNN-Transformer models,and lightweight implementations for edge computing.Our review encompasses recent studies demonstrating exceptional performance,with models like ECGformer achieving 98%accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97%accuracy with only 6k parameters suitable for wearable devices.We discuss key advantages including the ability to capture long-range dependencies in ECG sequences,handle variable-length inputs,and integrate multi-lead spatial information through attention mechanisms.However,significant challenges remain,including high computational requirements,dependence on large labeled datasets,limited interpretability in clinical settings,and over-fitting r isks with imbalanced data.The paper explores emerging solutions such as transfer learning,data augmentation techniques,and explainable AI methods to address these limitations.Future prospects include the development of more efficient architectures for real-time monitoring,integration with multi-modal physiological data,and enhanced clinical interpretability.This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate,efficient,and clinically viable automated arrhythmia detection systems.展开更多
基金Shanghai Municipal Commission of Economy and Information Technology,China(No.202301054)。
文摘End-to-end object detection Transformer(DETR)successfully established the paradigm of the Transformer architecture in the field of object detection.Its end-to-end detection process and the idea of set prediction have become one of the hottest network architectures in recent years.There has been an abundance of work improving upon DETR.However,DETR and its variants require a substantial amount of memory resources and computational costs,and the vast number of parameters in these networks is unfavorable for model deployment.To address this issue,a greedy pruning(GP)algorithm is proposed,applied to a variant denoising-DETR(DN-DETR),which can eliminate redundant parameters in the Transformer architecture of DN-DETR.Considering the different roles of the multi-head attention(MHA)module and the feed-forward network(FFN)module in the Transformer architecture,a modular greedy pruning(MGP)algorithm is proposed.This algorithm separates the two modules and applies their respective optimal strategies and parameters.The effectiveness of the proposed algorithm is validated on the COCO 2017 dataset.The model obtained through the MGP algorithm reduces the parameters by 49%and the number of floating point operations(FLOPs)by 44%compared to the Transformer architecture of DN-DETR.At the same time,the mean average precision(mAP)of the model increases from 44.1%to 45.3%.
文摘Following the groundbreaking introduction of the Transformer architecture in 2017,the development of Large Language Models(LLMs)formally commenced.In May 2020,Chat GPT-3,with over one hundred billion parameters,entered the public eye,marking a significant milestone in LLM advancement.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.
文摘Social transformations have been changed in thinking and observable current religious institutions in contemporary architectural manifestations. Likewise, observed these changes in religious buildings of the past, they were current and contemporary in its own time, which has allowed them to remain in force in its architectural spatiality and its urban relationships, serving as the historical bridge 1;hat allows analyzing the contemporary perspective in Mexico and particularly in Yucatan. The goal of this paper is to carry out a contemporary look to the interior and exterior of the temples in Yucatan colonial architecture to establish the process of change and permanence of spatiality and volumes that have allowed liturgy procedures as well as the way of approach to the sacredness of the Catholic communities from the colonial period to the present day. On the other hand, interest to establish, in particular, as the colonials religious buildings have been maintained in force and have appreciated for religious service to continue and in some cases, as they have given other kinds of services while maintaining its primordial spatiality.
基金supported by the Changsha Municipal Natural Science Foundation(grant no.kq2014160)in part by the National Natural Science Foundation in China(grant no.61703441)+2 种基金in part by the Key Projects of the Department of Education,Hunan Province(grant no.19A511)in part by the Hunan Key Laboratory of Intelligent Logistics Technology(grant no.2019TP1015)in part by the National Natural Science Foundation of China(grant no.61902436).
文摘Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .
基金This work was supported by the National Natural Science Foundation of China (No.69773025) and Ph.D. Foundationof the Ministry
文摘Architecture transformations are frequently performed during software design and maiatenance. However this activity is not well supported at a sufficiently abstract level. In this paper, the authors characterize architecture transformations using graph rewriting rules, where architectures are represented in graph notations. Architectures are usually required to satisfy certain constraints during evolution. Therefore a way is presented to construct the suffi- cient and necessary condition for a transformation to preserve a constraint. The condition can be verified before the application of the transformation. Validated transformations are guaranteed not to violate corresponding constraints whenever applied.
文摘This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.
基金supported by the Henan Provincial Scientific and Technological Research Project(No.252102210005,222102310222)the Training Program for Young Backbone Teachers in Higher Education Institutions of Henan Province(No.2025GGJS149).
文摘The rapid advancement of deep learning has revolutionized electrocardiogram(ECG)analysis,with Transformer architectures emerging as powerful tools for automated arrhythmia classification.This paper presents a comprehensive review of Transformer-based arrhythmia classification methods,examining their evolution,current capabilities,and future potential.We systematically analyze the architectural adaptations of Transformers for ECG signal processing,including Vision Transformers adapted for 1D medical signals,hybrid CNN-Transformer models,and lightweight implementations for edge computing.Our review encompasses recent studies demonstrating exceptional performance,with models like ECGformer achieving 98%accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97%accuracy with only 6k parameters suitable for wearable devices.We discuss key advantages including the ability to capture long-range dependencies in ECG sequences,handle variable-length inputs,and integrate multi-lead spatial information through attention mechanisms.However,significant challenges remain,including high computational requirements,dependence on large labeled datasets,limited interpretability in clinical settings,and over-fitting r isks with imbalanced data.The paper explores emerging solutions such as transfer learning,data augmentation techniques,and explainable AI methods to address these limitations.Future prospects include the development of more efficient architectures for real-time monitoring,integration with multi-modal physiological data,and enhanced clinical interpretability.This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate,efficient,and clinically viable automated arrhythmia detection systems.
