The capability of error detection of patient-specific QA tools plays an important role in verifying MLC motion accuracy. The goal of this study was to investigate the capability in error detection of portal dosimetry,...The capability of error detection of patient-specific QA tools plays an important role in verifying MLC motion accuracy. The goal of this study was to investigate the capability in error detection of portal dosimetry, MapCHECK2 and MatriXX QA tools in IMRT plans. The 9 fields IMRT for 4 head and neck plans and 7 fields IMRT for 4 prostate plans were selected for the error detection of QA devices. The measurements were undertaken for the original plan and the modified plans, where the known errors were introduced for increasing and decreasing of prescribed dose (±2%, ±4% and ±6%) and position shifted in X-axis and Y-axis (±1, ±2, ±3 and ±5 mm). After measurement, the results were compared between calculated and measured values using gamma analysis at 3%/3 mm criteria. The average gamma pass for no errors introduced in head and neck plans was 96.9%, 98.6%, and 98.8%, while prostate plans presented 99.4%, 99.0%, and 99.7%, for portal dosimetry, MapCHECK2 and MatriXX system, respectively. In head and neck plan, the shifted error detections were 1 mm for portal dosimetry, 2 mm for MapCHECK2, and 3 mm for MatriXX system. In prostate plan, the shifted error detections were 2 mm for portal dosimetry, 3 mm for MapCHECK2, and 5 mm for MatriXX system. For the dose error detection, the portal dosimetry system could detect at 2% dose deviation in head and neck and 4% in prostate plans, while other two devices could detect at 4% dose deviation in both head and neck and prostate plans. Portal dosimetry shows slightly more capability to detect the error compared with MapCHECK2 and MatriXX system, especially in the complicated plan. It may be due to higher resolution of the detector;however, all three-detector types can detect various errors and can be used for patient-specific IMRT QA.展开更多
Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text generation.One of the relevant capabilities is contextual learning...Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text generation.One of the relevant capabilities is contextual learning,which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates.In recent years,the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior.In this study,we investigate the ability of different LLMs,ranging from zero-shot and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,it is found that the encoder-decoder model called Zephyr achieves the best results with the fine-tuning approach,scoring 86.811%on the Explainable Detection of Online Sexism(EDOS)test-set and 57.453%on the Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter(HatEval)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.展开更多
Zero-Shot object Detection(ZSD),one of the most challenging problems in the field of object detection,aims to accurately identify new categories that are not encountered during training.Recent advancements in deep lea...Zero-Shot object Detection(ZSD),one of the most challenging problems in the field of object detection,aims to accurately identify new categories that are not encountered during training.Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems,achieving high recognition accuracy on benchmark datasets.However,these systems remain limited in real-world applications due to the scarcity of labeled training samples,making it difficult to detect unseen classes.To address this,researchers have explored various approaches,yielding promising progress.This article provides a comprehensive review of the current state of ZSD,distinguishing four related methods—zero-shot,open-vocabulary,open-set,and open-world approaches—based on task objectives and data usage.We highlight representative methods,discuss the technical challenges within each framework,and summarize the commonly used evaluation metrics,benchmark datasets,and experimental results.Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD.展开更多
智能车辆人机协作的关键是以人为核心,换道作为最基本的驾驶任务之一,准确高效预测驾驶人换道意图对人机协作拟人化发展至关重要。本文基于驾驶人认知决策空间的理论,设计了驾驶人换道意图预测试验,分析了车辆操纵数据、驾驶人视觉特性...智能车辆人机协作的关键是以人为核心,换道作为最基本的驾驶任务之一,准确高效预测驾驶人换道意图对人机协作拟人化发展至关重要。本文基于驾驶人认知决策空间的理论,设计了驾驶人换道意图预测试验,分析了车辆操纵数据、驾驶人视觉特性与驾驶场景之间的关系,生成了驾驶人注视区与驾驶场景拓扑关系图,构建了不同时间窗口的驾驶人换道意图预测模型数据集,基于ConvNeXt(convolutional network)模型的逆残差深度可分离卷积,结合注意力机制ECA(efficient channel attention)、ConvLSTM(convolutional long short term memory)网络以及GCN(graph convolutional networks)图神经网络等结构,构建了基于注意力机制的驾驶人换道意图预测模型。结果表明,数据集时间宽度为3 s时模型的预测准确率表现最佳,为91.15%,通过对比试验、消融试验充分验证了所提出的基于注意力机制的驾驶人换道意图预测模型的优越性能。展开更多
Few-shot intent detection is a practical challenge task,because new intents are frequently emerging and collecting large-scale data for them could be costly.Meta-learning,a promising technique for leveraging data from...Few-shot intent detection is a practical challenge task,because new intents are frequently emerging and collecting large-scale data for them could be costly.Meta-learning,a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks,has been a popular way to tackle this problem.However,the existing meta-learning models have been evidenced to be overfitting when the meta-training tasks are insufficient.To overcome this challenge,we present a novel self-supervised task augmentation with meta-learning framework,namely STAM.Firstly,we introduce the task augmentation,which explores two different strategies and combines them to extend meta-training tasks.Secondly,we devise two auxiliary losses for integrating self-supervised learning into meta-learning to learn more generalizable and transferable features.Experimental results show that STAM can achieve consistent and considerable performance improvement to existing state-of-the-art methods on four datasets.展开更多
With the rapid development and popularization of Internet technology,the propagation and diffusion of information become much easier and faster.While making life more convenient,the Internet also promotes the wide spr...With the rapid development and popularization of Internet technology,the propagation and diffusion of information become much easier and faster.While making life more convenient,the Internet also promotes the wide spread of fake news,which will have a great negative impact on countries,societies,and individuals.Therefore,a lot of research efforts have been made to combat fake news.Fake news detection is typically a classification problem aiming at verifying the veracity of news contents,which may include texts,images and videos.This article provides a comprehensive survey of fake news detection.We first summarize three intrinsic characteristics of fake news by analyzing its entire diffusion process,namely intentional creation,heteromorphic transmission,and controversial reception.The first refers to why users publish fake news,the second denotes how fake news propagates and distributes,and the last means what viewpoints different users may hold for fake news.We then discuss existing fake news detection approaches according to these characteristics.Thus,this review will enable readers to better understand this field from a new perspective.We finally discuss the trends of technological advances in this field and also outline some potential directions for future research.展开更多
针对当前车辆变道轨迹预测精度不足的问题,基于注意力长短期记忆网络(Attention-Long Short Term Memory,Attention-LSTM)提出一种融合变道意图识别的车辆变道轨迹预测模型。构建基于期望最大化-隐马尔可夫(Expectation Maximum-Hidden ...针对当前车辆变道轨迹预测精度不足的问题,基于注意力长短期记忆网络(Attention-Long Short Term Memory,Attention-LSTM)提出一种融合变道意图识别的车辆变道轨迹预测模型。构建基于期望最大化-隐马尔可夫(Expectation Maximum-Hidden Markov Model,EM-HMM)算法的变道意图识别模型,在模型中引入短期驾驶风格检测网络,以提升识别精度;在此基础上提出一种融合变道意图识别的变道轨迹预测模型,对模型的网络结构进行深度优化,以进一步提升模型预测精度;最后结合实车数据进行验证实验。实验结果表明:引入短期驾驶风格检测网络的变道意图识别模型的识别准确率达92.3%;经网络结构优化后的变道轨迹预测模型,对于车辆向右变道时的横向轨迹预测偏差降低至0.33 m,向左变道时的横向轨迹预测偏差降低至0.22 m。展开更多
为解决经典GOF设计模式扩展后不便于恢复的问题,结合Petterson提出的设计模式变体思想与Scanniello提出的设计模式复用概念,提出一种注入间接线索的设计模式变体检测方法,在遵循GOF标准设计模式与其变体意图一致性原则基础上,以类及其...为解决经典GOF设计模式扩展后不便于恢复的问题,结合Petterson提出的设计模式变体思想与Scanniello提出的设计模式复用概念,提出一种注入间接线索的设计模式变体检测方法,在遵循GOF标准设计模式与其变体意图一致性原则基础上,以类及其关系为基础,关注参与角色间有价值的间接联系,给出了创建型、行为型、结构型模式变体的具体实现,并依次以Factory M ethod、Command、Proxy模式变体为例,通过6种主流工具与4种经典开源系统对三种设计模式变体进行了检测比较,实验结果表明,本研究有助于设计模式解决方案的恢复.展开更多
文摘The capability of error detection of patient-specific QA tools plays an important role in verifying MLC motion accuracy. The goal of this study was to investigate the capability in error detection of portal dosimetry, MapCHECK2 and MatriXX QA tools in IMRT plans. The 9 fields IMRT for 4 head and neck plans and 7 fields IMRT for 4 prostate plans were selected for the error detection of QA devices. The measurements were undertaken for the original plan and the modified plans, where the known errors were introduced for increasing and decreasing of prescribed dose (±2%, ±4% and ±6%) and position shifted in X-axis and Y-axis (±1, ±2, ±3 and ±5 mm). After measurement, the results were compared between calculated and measured values using gamma analysis at 3%/3 mm criteria. The average gamma pass for no errors introduced in head and neck plans was 96.9%, 98.6%, and 98.8%, while prostate plans presented 99.4%, 99.0%, and 99.7%, for portal dosimetry, MapCHECK2 and MatriXX system, respectively. In head and neck plan, the shifted error detections were 1 mm for portal dosimetry, 2 mm for MapCHECK2, and 3 mm for MatriXX system. In prostate plan, the shifted error detections were 2 mm for portal dosimetry, 3 mm for MapCHECK2, and 5 mm for MatriXX system. For the dose error detection, the portal dosimetry system could detect at 2% dose deviation in head and neck and 4% in prostate plans, while other two devices could detect at 4% dose deviation in both head and neck and prostate plans. Portal dosimetry shows slightly more capability to detect the error compared with MapCHECK2 and MatriXX system, especially in the complicated plan. It may be due to higher resolution of the detector;however, all three-detector types can detect various errors and can be used for patient-specific IMRT QA.
基金This work is part of the research projects LaTe4PoliticES(PID2022-138099OBI00)funded by MICIU/AEI/10.13039/501100011033the European Regional Development Fund(ERDF)-A Way of Making Europe and LT-SWM(TED2021-131167B-I00)funded by MICIU/AEI/10.13039/501100011033the European Union NextGenerationEU/PRTR.Mr.Ronghao Pan is supported by the Programa Investigo grant,funded by the Region of Murcia,the Spanish Ministry of Labour and Social Economy and the European Union-NextGenerationEU under the“Plan de Recuperación,Transformación y Resiliencia(PRTR).”。
文摘Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text generation.One of the relevant capabilities is contextual learning,which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates.In recent years,the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior.In this study,we investigate the ability of different LLMs,ranging from zero-shot and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,it is found that the encoder-decoder model called Zephyr achieves the best results with the fine-tuning approach,scoring 86.811%on the Explainable Detection of Online Sexism(EDOS)test-set and 57.453%on the Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter(HatEval)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.
基金supported by the National Natural Science Foundation of China(Nos.62106150 and 62272315)the Open Fund of National Engineering Laboratory for Big Data System Computing Technology(No.SZU-BDSC-OF2024-22)+1 种基金the Open Research Fund of Anhui Province Key Laboratory of Machine Vision Inspection(No.KLMVI-2023-HIT-01)the Director Fund of Guangdong Laboratory of Artificial Intelligence and Digital Economy(Shenzhen)(No.24420001).
文摘Zero-Shot object Detection(ZSD),one of the most challenging problems in the field of object detection,aims to accurately identify new categories that are not encountered during training.Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems,achieving high recognition accuracy on benchmark datasets.However,these systems remain limited in real-world applications due to the scarcity of labeled training samples,making it difficult to detect unseen classes.To address this,researchers have explored various approaches,yielding promising progress.This article provides a comprehensive review of the current state of ZSD,distinguishing four related methods—zero-shot,open-vocabulary,open-set,and open-world approaches—based on task objectives and data usage.We highlight representative methods,discuss the technical challenges within each framework,and summarize the commonly used evaluation metrics,benchmark datasets,and experimental results.Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD.
文摘智能车辆人机协作的关键是以人为核心,换道作为最基本的驾驶任务之一,准确高效预测驾驶人换道意图对人机协作拟人化发展至关重要。本文基于驾驶人认知决策空间的理论,设计了驾驶人换道意图预测试验,分析了车辆操纵数据、驾驶人视觉特性与驾驶场景之间的关系,生成了驾驶人注视区与驾驶场景拓扑关系图,构建了不同时间窗口的驾驶人换道意图预测模型数据集,基于ConvNeXt(convolutional network)模型的逆残差深度可分离卷积,结合注意力机制ECA(efficient channel attention)、ConvLSTM(convolutional long short term memory)网络以及GCN(graph convolutional networks)图神经网络等结构,构建了基于注意力机制的驾驶人换道意图预测模型。结果表明,数据集时间宽度为3 s时模型的预测准确率表现最佳,为91.15%,通过对比试验、消融试验充分验证了所提出的基于注意力机制的驾驶人换道意图预测模型的优越性能。
基金the National Natural Science Foundation of China under Grant Nos.61936012 and 61976114。
文摘Few-shot intent detection is a practical challenge task,because new intents are frequently emerging and collecting large-scale data for them could be costly.Meta-learning,a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks,has been a popular way to tackle this problem.However,the existing meta-learning models have been evidenced to be overfitting when the meta-training tasks are insufficient.To overcome this challenge,we present a novel self-supervised task augmentation with meta-learning framework,namely STAM.Firstly,we introduce the task augmentation,which explores two different strategies and combines them to extend meta-training tasks.Secondly,we devise two auxiliary losses for integrating self-supervised learning into meta-learning to learn more generalizable and transferable features.Experimental results show that STAM can achieve consistent and considerable performance improvement to existing state-of-the-art methods on four datasets.
基金supported in part by the National Natural Science Foundation of China Science Fund for Creative Research Groups(62121002)Excellent Young Scientists Fund(62222212).
文摘With the rapid development and popularization of Internet technology,the propagation and diffusion of information become much easier and faster.While making life more convenient,the Internet also promotes the wide spread of fake news,which will have a great negative impact on countries,societies,and individuals.Therefore,a lot of research efforts have been made to combat fake news.Fake news detection is typically a classification problem aiming at verifying the veracity of news contents,which may include texts,images and videos.This article provides a comprehensive survey of fake news detection.We first summarize three intrinsic characteristics of fake news by analyzing its entire diffusion process,namely intentional creation,heteromorphic transmission,and controversial reception.The first refers to why users publish fake news,the second denotes how fake news propagates and distributes,and the last means what viewpoints different users may hold for fake news.We then discuss existing fake news detection approaches according to these characteristics.Thus,this review will enable readers to better understand this field from a new perspective.We finally discuss the trends of technological advances in this field and also outline some potential directions for future research.
文摘针对当前车辆变道轨迹预测精度不足的问题,基于注意力长短期记忆网络(Attention-Long Short Term Memory,Attention-LSTM)提出一种融合变道意图识别的车辆变道轨迹预测模型。构建基于期望最大化-隐马尔可夫(Expectation Maximum-Hidden Markov Model,EM-HMM)算法的变道意图识别模型,在模型中引入短期驾驶风格检测网络,以提升识别精度;在此基础上提出一种融合变道意图识别的变道轨迹预测模型,对模型的网络结构进行深度优化,以进一步提升模型预测精度;最后结合实车数据进行验证实验。实验结果表明:引入短期驾驶风格检测网络的变道意图识别模型的识别准确率达92.3%;经网络结构优化后的变道轨迹预测模型,对于车辆向右变道时的横向轨迹预测偏差降低至0.33 m,向左变道时的横向轨迹预测偏差降低至0.22 m。
文摘为解决经典GOF设计模式扩展后不便于恢复的问题,结合Petterson提出的设计模式变体思想与Scanniello提出的设计模式复用概念,提出一种注入间接线索的设计模式变体检测方法,在遵循GOF标准设计模式与其变体意图一致性原则基础上,以类及其关系为基础,关注参与角色间有价值的间接联系,给出了创建型、行为型、结构型模式变体的具体实现,并依次以Factory M ethod、Command、Proxy模式变体为例,通过6种主流工具与4种经典开源系统对三种设计模式变体进行了检测比较,实验结果表明,本研究有助于设计模式解决方案的恢复.
