The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, ...The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective.展开更多
With the advancement of content-based retrieval technology, the importance of semantics for text information contained in images attracts many researchers. An algorithm which will automatically locate the textual regi...With the advancement of content-based retrieval technology, the importance of semantics for text information contained in images attracts many researchers. An algorithm which will automatically locate the textual regions in the input image will facilitate the retrieving task, and the optical character recognizer can then be applied to only those regions of the image which contain text. In this paper a new text location method based wavelet is described, which can be used to locate textual regions from complex image and video frame. Experimental results show that the textual regions in image can be located effectively and quickly.展开更多
Location-based social networks have attracted increasing users in recent years. Human movements and mobility patterns have a high degree of freedom and provide us with a lot of trajectory to understand the activity of...Location-based social networks have attracted increasing users in recent years. Human movements and mobility patterns have a high degree of freedom and provide us with a lot of trajectory to understand the activity of users. In this paper, we present?a user preferences and time sensitive recommender systems that offer an appropriate venue for a user when he appears in a special time at a particular location. The system considering the factors are: 1) the popularity of a location;2) the preferences of a user;3) social influence of the friends of the user and the friends who are check-in at the same location with the user;and 4) the time feature of the location and the user visiting. We evaluate our system with a large-scale real dataset from a location-based social network of Gowalla. The results confirm that our method provides more accurate location recommendations compared to the baseline.展开更多
针对传统基于阅读理解框架的命名实体识别(NER)方法存在的单条样本实体数量稀释以及在预测实体头尾时缺乏对实体完整位置信息的利用这两方面问题,本文基于阅读理解框架提出一种融合实体头尾关键特征的医学文本命名实体识别模型IKFSE(int...针对传统基于阅读理解框架的命名实体识别(NER)方法存在的单条样本实体数量稀释以及在预测实体头尾时缺乏对实体完整位置信息的利用这两方面问题,本文基于阅读理解框架提出一种融合实体头尾关键特征的医学文本命名实体识别模型IKFSE(integrated key feature of entity start and end).首先,设计一种实体头尾关键特征提取模块,提取出针对医学实体起始位置和结束位置的关键特征,减少冗余信息对模型的影响;其次,设计一种实体头尾特征交叉融合模块,在对实体起始位置和结束位置进行预测时分别引入二者对彼此的影响,从而引入实体完整的位置信息,提高模型的语义表征能力.在cEHRNER和CCKS2017两个公开数据集上将IKFSE与多个主流序列标注模型和阅读理解模型相比,结果表明本文所提方法在中文医学NER任务中有着更好的性能.展开更多
随着位置社交网络(location-based social network,LBSN)的快速增长,兴趣点(point-ofinterest,POI)推荐已经成为一种帮助人们发现有趣位置的重要方式.现有的研究工作主要是利用用户签到的历史数据及其情景信息(如地理信息、社交关系)来...随着位置社交网络(location-based social network,LBSN)的快速增长,兴趣点(point-ofinterest,POI)推荐已经成为一种帮助人们发现有趣位置的重要方式.现有的研究工作主要是利用用户签到的历史数据及其情景信息(如地理信息、社交关系)来提高推荐质量,而忽视了利用兴趣点相关的评论信息.但是,现实中用户在LBSN中只对少数兴趣点进行签到,使得用户签到历史数据及其情景信息极其稀疏,这对兴趣点推荐来说是一个巨大的挑战.为此,提出了一种新的兴趣点推荐模型,称为GeoSoRev模型.该模型在已有的基于矩阵分解的经典推荐模型的基础上,融合关于兴趣点的评论信息、用户社交关联和地理信息这3个因素进行兴趣点推荐.基于2个来自Foursquare的真实数据集的实验结果表明,与其他主流的兴趣点推荐模型相比,GeoSoRev模型在准确率和召回率等多项评价指标上都取得了显著的提高.展开更多
基金Supported by the National Natural Science Foundation of China(No.60402036)the Natural Science Foundation of Beijing(No.4042008).
文摘The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective.
文摘With the advancement of content-based retrieval technology, the importance of semantics for text information contained in images attracts many researchers. An algorithm which will automatically locate the textual regions in the input image will facilitate the retrieving task, and the optical character recognizer can then be applied to only those regions of the image which contain text. In this paper a new text location method based wavelet is described, which can be used to locate textual regions from complex image and video frame. Experimental results show that the textual regions in image can be located effectively and quickly.
文摘Location-based social networks have attracted increasing users in recent years. Human movements and mobility patterns have a high degree of freedom and provide us with a lot of trajectory to understand the activity of users. In this paper, we present?a user preferences and time sensitive recommender systems that offer an appropriate venue for a user when he appears in a special time at a particular location. The system considering the factors are: 1) the popularity of a location;2) the preferences of a user;3) social influence of the friends of the user and the friends who are check-in at the same location with the user;and 4) the time feature of the location and the user visiting. We evaluate our system with a large-scale real dataset from a location-based social network of Gowalla. The results confirm that our method provides more accurate location recommendations compared to the baseline.
文摘针对传统基于阅读理解框架的命名实体识别(NER)方法存在的单条样本实体数量稀释以及在预测实体头尾时缺乏对实体完整位置信息的利用这两方面问题,本文基于阅读理解框架提出一种融合实体头尾关键特征的医学文本命名实体识别模型IKFSE(integrated key feature of entity start and end).首先,设计一种实体头尾关键特征提取模块,提取出针对医学实体起始位置和结束位置的关键特征,减少冗余信息对模型的影响;其次,设计一种实体头尾特征交叉融合模块,在对实体起始位置和结束位置进行预测时分别引入二者对彼此的影响,从而引入实体完整的位置信息,提高模型的语义表征能力.在cEHRNER和CCKS2017两个公开数据集上将IKFSE与多个主流序列标注模型和阅读理解模型相比,结果表明本文所提方法在中文医学NER任务中有着更好的性能.
文摘随着位置社交网络(location-based social network,LBSN)的快速增长,兴趣点(point-ofinterest,POI)推荐已经成为一种帮助人们发现有趣位置的重要方式.现有的研究工作主要是利用用户签到的历史数据及其情景信息(如地理信息、社交关系)来提高推荐质量,而忽视了利用兴趣点相关的评论信息.但是,现实中用户在LBSN中只对少数兴趣点进行签到,使得用户签到历史数据及其情景信息极其稀疏,这对兴趣点推荐来说是一个巨大的挑战.为此,提出了一种新的兴趣点推荐模型,称为GeoSoRev模型.该模型在已有的基于矩阵分解的经典推荐模型的基础上,融合关于兴趣点的评论信息、用户社交关联和地理信息这3个因素进行兴趣点推荐.基于2个来自Foursquare的真实数据集的实验结果表明,与其他主流的兴趣点推荐模型相比,GeoSoRev模型在准确率和召回率等多项评价指标上都取得了显著的提高.
