With focus now placed on the learner, more attention is given to his learning style, multiple intelligence and developing learning strategies to enable him to make sense of and use of the target language appropriately...With focus now placed on the learner, more attention is given to his learning style, multiple intelligence and developing learning strategies to enable him to make sense of and use of the target language appropriately in varied contexts and with different uses of the language. To attain this, the teacher is tasked with designing, monitoring and processing language learning activities for students to carry out and in the process learn by doing and reflecting on the learning process they went through as they interacted socially with each other. This paper describes a task named"The Fishbowl Technique"and found to be effective in large ESL classes in the secondary level in the Philippines.展开更多
Traditionally, heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites. However, the traditional heuristic strategies depend on the concrete tasks, which often ...Traditionally, heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites. However, the traditional heuristic strategies depend on the concrete tasks, which often affect the result’s optimality. Noticing that the historical information of cooperative task planning will impact the latter planning results, we propose a hybrid learning algorithm for dynamic multi-satellite task planning, which is based on the multi-agent reinforcement learning of policy iteration and the transfer learning. The reinforcement learning strategy of each satellite is described with neural networks. The policy neural network individuals with the best topological structure and weights are found by applying co-evolutionary search iteratively. To avoid the failure of the historical learning caused by the randomly occurring observation requests, a novel approach is proposed to balance the quality and efficiency of the task planning, which converts the historical learning strategy to the current initial learning strategy by applying the transfer learning algorithm. The simulations and analysis show the feasibility and adaptability of the proposed approach especially for the situation with randomly occurring observation requests.展开更多
Recently, several deep learning models have been successfully proposed and have been applied to solve different Natural Language Processing (NLP) tasks. However, these models solve the problem based on single-task sup...Recently, several deep learning models have been successfully proposed and have been applied to solve different Natural Language Processing (NLP) tasks. However, these models solve the problem based on single-task supervised learning and do not consider the correlation between the tasks. Based on this observation, in this paper, we implemented a multi-task learning model to joint learn two related NLP tasks simultaneously and conducted experiments to evaluate if learning these tasks jointly can improve the system performance compared with learning them individually. In addition, a comparison of our model with the state-of-the-art learning models, including multi-task learning, transfer learning, unsupervised learning and feature based traditional machine learning models is presented. This paper aims to 1) show the advantage of multi-task learning over single-task learning in training related NLP tasks, 2) illustrate the influence of various encoding structures to the proposed single- and multi-task learning models, and 3) compare the performance between multi-task learning and other learning models in literature on textual entailment task and semantic relatedness task.展开更多
In recent years, China's education reform has been continuously promoted, especially the implementation of the new curriculum reform, which has made great progress in China's education modernization. Based on ...In recent years, China's education reform has been continuously promoted, especially the implementation of the new curriculum reform, which has made great progress in China's education modernization. Based on the requirements of the new curriculum reform, the concept of learning task group arises at the historic moment and is organically integrated with high school Chinese teaching, which plays a very important role in promoting the optimization of high school Chinese teaching contents and methods. In this paper, based on the "learning task group" of high school Chinese multi-text reading teaching to conduct an in-depth exploration, combined with the current development of high school Chinese multi-text reading teaching, put forward scientific and reasonable suggestions, in order to improve our country's high school Chinese education level, promote the modernization of our country's education to further develop, provide more reliable reference.展开更多
Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’...Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.展开更多
Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one....Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.展开更多
针对瓷砖色差检测中传统方法依赖主观目测导致的结果不稳定问题,提出一种融合纹理与颜色特征的瓷砖色差量化与分级方法。构建包含纹理与颜色双标签的大规模瓷砖数据集TILE-TCR(TILE Texture and Color Recognition),以提升模型对纹理与...针对瓷砖色差检测中传统方法依赖主观目测导致的结果不稳定问题,提出一种融合纹理与颜色特征的瓷砖色差量化与分级方法。构建包含纹理与颜色双标签的大规模瓷砖数据集TILE-TCR(TILE Texture and Color Recognition),以提升模型对纹理与颜色特征的表征能力;同时,构建色差分级数据集TILE-CAG(TILE Chromatic Aberration Grade),用于支持色差分类任务。在此基础上,改进分形编码网络(FENet)的网络结构,即引入空间金字塔池化(SPP)与SE(Squeeze-and-Excitation)模块,从而实现多任务特征提取与关键区域聚焦。然后,通过聚类算法自适应确定色差分级阈值,从而实现色差分级的客观量化。实验结果表明,所提改进方法在瓷砖纹理分类任务中的准确率达到92.82%,较FENet提升了1.84个百分点;在色差分级任务中所提方法的准确率、精确率、召回率和F1分数均超过90%。此外,还搭建了模拟生产流水线,以完成模型的工业部署与实时性测试。而所提方法在常见规格瓷砖上的平均检测时间低于3 s,满足工业传送带在线检测的实时性要求。展开更多
针对行人轨迹预测中存在的时序特征建模不足、多尺度融合缺乏明确区分以及多任务训练不稳定等问题,提出一种基于矩阵记忆长短期记忆网络(matrix long short-term memory, mLSTM)的纯时序预测算法。该算法构建以mLSTM为核心的编码器-解...针对行人轨迹预测中存在的时序特征建模不足、多尺度融合缺乏明确区分以及多任务训练不稳定等问题,提出一种基于矩阵记忆长短期记忆网络(matrix long short-term memory, mLSTM)的纯时序预测算法。该算法构建以mLSTM为核心的编码器-解码器架构,挖掘轨迹的时间依赖特征;设计多尺度轨迹特征融合模块,采用双向策略实现短期与长期特征的层次化表达;引入指数移动平均标准化的多任务机制,提升训练的稳定性与模型的泛化能力。在ETH和UCY数据集上的实验结果表明,该算法相较于Trajectory-Transformer和SGCN,在平均位移误差上分别降低14.81%和16.21%,在最终位移误差上分别降低19.66%和4.62%,展现出良好的预测精度与鲁棒性,为行人轨迹预测提供稳健有效的基础模型。展开更多
文摘With focus now placed on the learner, more attention is given to his learning style, multiple intelligence and developing learning strategies to enable him to make sense of and use of the target language appropriately in varied contexts and with different uses of the language. To attain this, the teacher is tasked with designing, monitoring and processing language learning activities for students to carry out and in the process learn by doing and reflecting on the learning process they went through as they interacted socially with each other. This paper describes a task named"The Fishbowl Technique"and found to be effective in large ESL classes in the secondary level in the Philippines.
文摘Traditionally, heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites. However, the traditional heuristic strategies depend on the concrete tasks, which often affect the result’s optimality. Noticing that the historical information of cooperative task planning will impact the latter planning results, we propose a hybrid learning algorithm for dynamic multi-satellite task planning, which is based on the multi-agent reinforcement learning of policy iteration and the transfer learning. The reinforcement learning strategy of each satellite is described with neural networks. The policy neural network individuals with the best topological structure and weights are found by applying co-evolutionary search iteratively. To avoid the failure of the historical learning caused by the randomly occurring observation requests, a novel approach is proposed to balance the quality and efficiency of the task planning, which converts the historical learning strategy to the current initial learning strategy by applying the transfer learning algorithm. The simulations and analysis show the feasibility and adaptability of the proposed approach especially for the situation with randomly occurring observation requests.
基金Supported by National Natural Science Foundation of China（60474035）,National Research Foundation for the Doctoral Program of Higher Education of China（20050359004）,Natural Science Foundation of Anhui Province（070412035）
基金Manuscript received March 5, 2010 accepted March 2, 2011 Supported by National Natural Science Foundation of China （61004103）, National Research Foundation for the Doctoral Program of Higher Education of China （20100111110005）, China Postdoctoral Science Foundation （20090460742）, and Natural Science Foundation of Anhui Province of China （090412058, 11040606Q44）
文摘Recently, several deep learning models have been successfully proposed and have been applied to solve different Natural Language Processing (NLP) tasks. However, these models solve the problem based on single-task supervised learning and do not consider the correlation between the tasks. Based on this observation, in this paper, we implemented a multi-task learning model to joint learn two related NLP tasks simultaneously and conducted experiments to evaluate if learning these tasks jointly can improve the system performance compared with learning them individually. In addition, a comparison of our model with the state-of-the-art learning models, including multi-task learning, transfer learning, unsupervised learning and feature based traditional machine learning models is presented. This paper aims to 1) show the advantage of multi-task learning over single-task learning in training related NLP tasks, 2) illustrate the influence of various encoding structures to the proposed single- and multi-task learning models, and 3) compare the performance between multi-task learning and other learning models in literature on textual entailment task and semantic relatedness task.
文摘In recent years, China's education reform has been continuously promoted, especially the implementation of the new curriculum reform, which has made great progress in China's education modernization. Based on the requirements of the new curriculum reform, the concept of learning task group arises at the historic moment and is organically integrated with high school Chinese teaching, which plays a very important role in promoting the optimization of high school Chinese teaching contents and methods. In this paper, based on the "learning task group" of high school Chinese multi-text reading teaching to conduct an in-depth exploration, combined with the current development of high school Chinese multi-text reading teaching, put forward scientific and reasonable suggestions, in order to improve our country's high school Chinese education level, promote the modernization of our country's education to further develop, provide more reliable reference.
文摘Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.
基金supported by National Key R&D Program of China(-NO.2017YFC0803700)National Nature Science Foundation of China(No.U1736206)+6 种基金National Nature Science Foundation of China(61671336)National Nature Science Foundation of China(61671332)Technology Research Program of Ministry of Public Security(No.2016JSYJA12)Hubei Province Technological Innovation Major Project(-No.2016AAA015)Hubei Province Technological Innovation Major Projec(2017AAA123)National Key Research and Development Program of China(No.2016YFB0100901)Nature Science Foundation of Jiangsu Province(No.BK20160386)
文摘Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.
基金supported by the National Key Research and Development Program of China(grant no.2025YFC3410200)the National Natural Science Foundation of China(grant nos.62433016,62572391,62402382,and 62102319).
文摘Spatial transcriptomics(ST)preserves spatial context in gene expression analysis yet faces limitations like low resolution and RNA capture inefficiency.To address these,we present stSCI,a computational method integrating single-cell(SC)and ST data into a unified,batch-corrected embedding space.
文摘针对瓷砖色差检测中传统方法依赖主观目测导致的结果不稳定问题,提出一种融合纹理与颜色特征的瓷砖色差量化与分级方法。构建包含纹理与颜色双标签的大规模瓷砖数据集TILE-TCR(TILE Texture and Color Recognition),以提升模型对纹理与颜色特征的表征能力;同时,构建色差分级数据集TILE-CAG(TILE Chromatic Aberration Grade),用于支持色差分类任务。在此基础上,改进分形编码网络(FENet)的网络结构,即引入空间金字塔池化(SPP)与SE(Squeeze-and-Excitation)模块,从而实现多任务特征提取与关键区域聚焦。然后,通过聚类算法自适应确定色差分级阈值,从而实现色差分级的客观量化。实验结果表明,所提改进方法在瓷砖纹理分类任务中的准确率达到92.82%,较FENet提升了1.84个百分点;在色差分级任务中所提方法的准确率、精确率、召回率和F1分数均超过90%。此外,还搭建了模拟生产流水线,以完成模型的工业部署与实时性测试。而所提方法在常见规格瓷砖上的平均检测时间低于3 s,满足工业传送带在线检测的实时性要求。
文摘针对行人轨迹预测中存在的时序特征建模不足、多尺度融合缺乏明确区分以及多任务训练不稳定等问题,提出一种基于矩阵记忆长短期记忆网络(matrix long short-term memory, mLSTM)的纯时序预测算法。该算法构建以mLSTM为核心的编码器-解码器架构,挖掘轨迹的时间依赖特征;设计多尺度轨迹特征融合模块,采用双向策略实现短期与长期特征的层次化表达;引入指数移动平均标准化的多任务机制,提升训练的稳定性与模型的泛化能力。在ETH和UCY数据集上的实验结果表明,该算法相较于Trajectory-Transformer和SGCN,在平均位移误差上分别降低14.81%和16.21%,在最终位移误差上分别降低19.66%和4.62%,展现出良好的预测精度与鲁棒性,为行人轨迹预测提供稳健有效的基础模型。
