空间矢量信息渐进传输研究与实现

为解决大量空间矢量数据的传输问题,提出一种空间矢量信息渐进传输解决方法。阐述空间矢量信息渐进传输的基本思想,包括空间矢量信息的综合、选取、概括、简化,设计数据库存储模式。以广东省行政区域图渐进传输的实现,证明该方法在处理大数据量传输时能使用户无网络延时感,且减少占用的网络带宽。     

基于网络的空间矢量信息渐进传输的研究

基于网络的空间矢量信息发布,仍存在大数据集浏览效果差强人意的问题,渐进传输是解决该问题的关键技术之一。在对空间矢量信息渐进传输的内在条件进行分析后,认为基于空间多尺度表达的渐进传输符合空间信息可视化的基本原则,而多尺度表达与空间信息概化关系密切。文章提出了基于空间信息概化和多尺度表达的渐进传输综合解决方案,该方案包括三个部分:采用空间信息概化方法获得不同尺度的地图;遵循多尺度表达的原则,运用结点LoD模型对不同尺度的地图进行处理和存储;综合Web Services、Plug-in和JavaScript脚本等技术设计、实现渐进传输体系结构。最后,文章基于新一代网络图形图像发布标准SVG,以广州市行政界线图为例在B/S模式下进行渐进传输实验,进一步评价了渐进传输综合解决方案在处理大数据集传输和自适应缩放等方面的可行性及有效性。     

Perceptual encryption with compression for secure vector map data processing

With the rapidly rising interest in geographic information system (GIS) contents, a large volume of valuable map data has been unlawfully distributed by pirates. Therefore, the secure storage and transmission of classified national digital map datasets have been increasingly threatened. As the importance of secure, large-volume map datasets has increased, vector map security techniques that focus on secure network and data encryption have been studied. These techniques are required to ensure access control and prevent illegal copying of digital maps. This paper presents perceptual encryption on the vector compression domain for copy protection and access control of vector maps. Our algorithm compresses all vector data of polylines and polygons by lossless minimum coding object (MCO) units and perceptually encrypts using two processes using the mean points and directions of MCOs. The first process changes the position of vector data by randomly permuting the mean points of MCOs, the so-called position encryption. The second process changes the geographic shape by circularly encrypting the directions of vertices in MCOs by the XOR operator. Experimental results have verified that our algorithm can encrypt GIS digital maps effectively and simply and can also improve the compression ratio, unlike general data encryption techniques, and thus, our algorithm is very effective for a large volume of GIS datasets.     

P2P架构的地图库管理模型

地图库管理着海量的空间数据,传统的客户机/服务器(C/S)架构已经不能满足日益增长的数据共享需求。提出一种基于peer-to-pee(rP2P)架构,以分布式哈希表(DHT)和空间数据索引技术为基础的方案,把地图库中的空间数据自动发布到P2P环境中进行管理。试验结果表明,P2P架构能够支持大量用户对地图库的并发访问。     

MCGIM-Based Model Streaming for Realtime Progressive Rendering

While most mesh streaming techniques focus on optimizing the transmission order of the polygon data,few approaches have addressed the streaming problems by using geometry images(GIM).In this paper,we present a new approach which firstly partitions a mesh into several surface patches,then converts these patches into multi-chart geometry images(MCGIM).After resampling the MCGIM and normal map atlas are obtained,we hierarchically construct the regular geometry image representation by adopting the quadtree structure.In this way,the encoded nodes can be transmitted in arbitrary order with high transmission flexibility.Also,the rendering quality of the partially transmitted models can be greatly improved by using the normal texture atlas.Meanwhile only the geometry on the silhouette to the current viewpoint are required to be refined and transmitted,therefore the amount of data is minimized for transferring each frame.In particular,our approach also allows users to encode and transmit the mesh data via JPEG2000 technique.Therefore,our mesh streaming method is suitable for transmitting 3D animation models with use of Motion JPEG2000 videos.Experimental results have demonstrated the effectiveness of our approach,which enables one server to stream the MCGIM texture atlas to the clients.Also,the transmitted model can be rendered in a multiresolution manner by GPU acceleration on the client side,due to the regular geometry structure of MCGIM.     

A general stochastic clustering method for automatic cluster discovery

Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the number of natural clusters hidden in the dataset. The problem is exacerbated when there is little or no additional information except the data itself. This paper proposes a general stochastic clustering method that is a simplification of nature-inspired ant-based clustering approach. It begins with a basic solution and then performs stochastic search to incrementally improve the solution until the underlying clusters emerge, resulting in automatic cluster discovery in datasets. This method differs from several recent methods in that it does not require users to input the number of clusters and it makes no explicit assumption about the underlying distribution of a dataset. Our experimental results show that the proposed method performs better than several existing methods in terms of clustering accuracy and efficiency in majority of the datasets used in this study. Our theoretical analysis shows that the proposed method has linear time and space complexities, and our empirical study shows that it can accurately and efficiently discover clusters in large datasets in which many existing methods fail to run.     

分布式GIS地图数据的组织与发布

分布式GIS中，地图数据的组织发布是一个关键性问题，针对流行的基于Internet的分布式GIS概念，文章在一个扩展性比较强的架构基础上，提出了一种开放的、贴近用户漫游体验的地图数据的组织、发布的解决方案。     

基于图游走的并行协同过滤推荐算法

针对目前协同过滤推荐算法存在的数据稀疏性问题和可扩展性问题,本文进行了相关研究。针对稀疏性问题,在传统的皮尔逊相关相似度中引入交占比系数计算用户间直接相似度,该方法缓解了用户间共同评分项的占比问题;提出一种基于图游走的间接相似度计算方法,该方法根据用户间的直接相似度建立用户网络图,在用户网络图上通过游走计算用户间的间接相似度,并进行推荐。在Spark平台上实现本文方法的并行化,缓解了数据规模增加带来的可扩展性问题。实验结果表明:本文提出的算法在不同数据集上均取得了良好效果,有效地提高了推荐准确度,并且在分布式环境下具有良好的可扩展性。     

A secure and privacy-preserving word vector training scheme based on functional encryption with inner-product predicates

With the extensive applications of machine learning, it has been witnessed that machine learning has been applied in various fields such as e-commerce, mobile data processing, health analytics and behavioral analytics etc. Word vector training is usually deployed in machine learning to provide a model architecture and optimization, for example, to learn word embeddings from a large amount of datasets. Training word vector in machine learning needs a lot of datasets to train and then outputs a model, however, some of which might contain private and sensitive information, and the training phase will lead to the exposure of the trained model and user datasets. In order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. For instance, the user data might contain of face,irirs and personal identities etc. This will release serious problem in the machine learning. In this article, we investigate the problem of training high-quality word vectors on encrypted datasets by using privacy-preserving learning algorithms. Firstly, we use a pseudo-random function to generate a statistical token for each word to help build the vocabulary of the word vector. Then we employ functional inner-product encryption to calculate the activation function to obtain the inner product, securely. Finally, we use BGN cryptosystem to encrypt and hide the sensitive datasets, and complete the homomorphic operation over the ciphertexts to perform the training procedure. In order to implement the privacy preservation of word vector training, we propose four privacy-preserving machine learning schemes to provide the privacy protection in our scheme. We analyze the security and efficiency of our protocols and give the numerical experiments. Compared with the existing solutions, it indicates that our scheme can provide a higher efficiency and less communication overhead.