A constrained partial permutation strategy is proposed for matching spatial relation graph (SRG), which is used in our sketch input and recognition system Smart Sketchpad for representing the spatial relationship amon...A constrained partial permutation strategy is proposed for matching spatial relation graph (SRG), which is used in our sketch input and recognition system Smart Sketchpad for representing the spatial relationship among the components of a graphic object. Using two kinds of matching constraints dynamically generated in the matching process, the proposed approach can prune most improper mappings between SRGs during the matching process. According to our theoretical analysis in this paper, the time complexity of our approach is O(n 2) in the best case, and O(n!) in the worst case, which occurs infrequently. The spatial complexity is always O(n) for all cases. Implemented in Smart Sketchpad, our proposed strategy is of good performance.展开更多
在云计算作为辅助的电子医疗系统中,患者的电子医疗记录(Electronic Healthcare Records,EHRs)通常会外包给云服务器提供商(Cloud Server Provider,CSP),其中EHRs一般会以加密的形式上传到云服务器,再通过可搜索加密方案进行搜索.然而,...在云计算作为辅助的电子医疗系统中,患者的电子医疗记录(Electronic Healthcare Records,EHRs)通常会外包给云服务器提供商(Cloud Server Provider,CSP),其中EHRs一般会以加密的形式上传到云服务器,再通过可搜索加密方案进行搜索.然而,由于过度依赖于被认为可完全信任的中心化服务器,现有的大多数可搜索加密方案仍面临着严重的安全问题.论文提出了一个面向医疗系统的区块链的可搜索加密方案,它不仅可以确保EHRs的安全,还可以提高存储在云服务器上的密码文本的搜索效率.在方案中,患者可以利用智能合约构建自动执行与自动查找的算法,这使医生收到可信的、正确的搜索结果.同时,方案采用了基于关键词转换的高效的模糊多关键词可搜索加密,优化EHRs的提取方式进而减少计算开销.此外,方案做了安全性分析和性能评估,证明方案的有效性和安全性.展开更多
A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classif...A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.展开更多
文摘A constrained partial permutation strategy is proposed for matching spatial relation graph (SRG), which is used in our sketch input and recognition system Smart Sketchpad for representing the spatial relationship among the components of a graphic object. Using two kinds of matching constraints dynamically generated in the matching process, the proposed approach can prune most improper mappings between SRGs during the matching process. According to our theoretical analysis in this paper, the time complexity of our approach is O(n 2) in the best case, and O(n!) in the worst case, which occurs infrequently. The spatial complexity is always O(n) for all cases. Implemented in Smart Sketchpad, our proposed strategy is of good performance.
文摘在云计算作为辅助的电子医疗系统中,患者的电子医疗记录(Electronic Healthcare Records,EHRs)通常会外包给云服务器提供商(Cloud Server Provider,CSP),其中EHRs一般会以加密的形式上传到云服务器,再通过可搜索加密方案进行搜索.然而,由于过度依赖于被认为可完全信任的中心化服务器,现有的大多数可搜索加密方案仍面临着严重的安全问题.论文提出了一个面向医疗系统的区块链的可搜索加密方案,它不仅可以确保EHRs的安全,还可以提高存储在云服务器上的密码文本的搜索效率.在方案中,患者可以利用智能合约构建自动执行与自动查找的算法,这使医生收到可信的、正确的搜索结果.同时,方案采用了基于关键词转换的高效的模糊多关键词可搜索加密,优化EHRs的提取方式进而减少计算开销.此外,方案做了安全性分析和性能评估,证明方案的有效性和安全性.
文摘A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.
