Topology authentication for piping isometric drawings

Piping isometric drawings, which feature their intrinsical topological relation rather than just geometrical shape, are important industrial art works in the field of Computer-Aided Design (CAD). This paper takes a fresh look at the topology integrity authentication of piping isometric drawings, which has not been mentioned before in the literature, from the digital watermarking perspective. A blind and semi-fragile watermarking based algorithm is proposed to address the referred interesting issue. The topology authentication problem of piping isometric drawings is investigated. In addition to the stretching operation, both global and local similarity transformation operations, which are critical problems in the case of watermarking embedding and extraction, are analyzed in detail. The topological graph is extracted and constructed from the drawing firstly. Then, similarity transformation invariants are constructed as watermarks carriers for each node. After that, the topological relation among joint components is encoded into singular watermarks for each node of the graph. These generated topology sensitive watermarks are embedded into geometrical invariants of each node via quantization index modulation. Theoretical analysis and experimental results demonstrate that our approach yields a strong ability in detecting and locating unauthorized topology attacks while achieves robustness against both global and local similarity transformations especially the stretching operation. The proposed scheme can be employed to authenticate topology integrity for each of the drawings derived from the model individually in industry practices.     

Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations

In this paper we present a methodology to analyze and visualize streams of Social Media messages and apply it to a case in which Twitter is used as a backchannel, i.e. as a communication medium through which participants follow an event in the real world as it unfolds. Unlike other methods based on social networks or theories of information diffusion, we do not assume proximity or a pre-existing social structure to model content generation and diffusion by distributed users; instead we refer to concepts and theories from discourse psychology and conversational analysis to track online interaction and discover how people collectively make sense of novel events through micro-blogging. In particular, the proposed methodology extracts concept maps from twitter streams and uses a mix of sentiment and topological metrics computed over the extracted concept maps to build visual devices and display the conversational flow represented as a trajectory through time of automatically extracted topics. We evaluated the proposed method through data collected from the analysis of Twitter users’ reactions to the March 2015 Apple Keynote during which the company announced the official launch of several new products.     

Efficient and Dynamic Simplification of Line Drawings

In this paper we present a pipeline for rendering dynamic 2D/3D line drawings efficiently. Our main goal is to create efficient static renditions and coherent animations of line drawings in a setting where lines can be added, deleted and arbitrarily transformed on‐the‐fly. Such a dynamic setting enables us to handle interactively sketched 2D line data, as well as arbitrarily transformed 3D line data in a unified manner. We evaluate the proximity of screen projected strokes to simplify them while preserving their continuity. We achieve this by using a special data structure that facilitates efficient proximity calculations in a dynamic setting. This on‐the‐fly proximity evaluation also facilitates generation of appropriate visibility cues to mitigate depth ambiguities and visual clutter for 3D line data. As we perform all these operations using only line data, we can create line drawings from 3D models without any surface information. We demonstrate the effectiveness and applicability of our approach by showing several examples with initial line representations obtained from a variety of sources: 2D and 3D hand‐drawn sketches and 3D salient geometry lines obtained from 3D surface representations.     

加权迭代节点匹配算法及其在语言网络中的应用

复杂网络间节点匹配在很多领域中均具有重要现实意义。然而,传统的节点匹配算法通常只利用网络的局部拓扑信息,在对拥有高对称性的真实网络作用时往往会失效。为了克服这一缺点,我们近期利用网络拓扑信息和连边权重信息,提出了一种新型的同时来计算不同网络间节点相似度的方法,并在此基础上设计了一种加权迭代节点匹配算法。将该算法在高度拓扑对称仿真网络对和真实中英文语言网络对上分别进行了测试,结果表明加权迭代节点匹配算法在此类网络上优于纯拓扑迭代节点匹配算法。     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

基于对象图例及其拓扑关系识别的二维工程CAD图纸矢量化方法

为实现二维工程CAD图纸矢量化,提出一种基于对象图例及其拓扑关系识别的矢量化方法。该方法首先针对对象图例的几何属性,提出基于HOG(Histogram of Gradient)和SVM(Support Vector Machine)的多类对象图例分类方法,然后通过提取对象的环形分割特征识别子类对象图例,接着利用基于连通域标记方法实现对象图例拓扑关系的识别。结果表明,本文算法能够有效识别图纸中的对象及其拓扑关系,对于图纸中常见的图例线条断裂、模糊等问题具有鲁棒性。基于本文算法提取的对象几何和拓扑信息可以为后续的图纸矢量化奠定基础,相关关键技术的探索性研究对后续工程图纸矢量化研究具有一定启发性。     

Mesh repair with user-friendly topology control

Limitations of current 3D acquisition technology often lead to polygonal meshes exhibiting a number of geometrical and topological defects which prevent them from widespread use. In this paper we present a new method for model repair which takes as input an arbitrary polygonal mesh and outputs a valid 2-manifold triangle mesh. Unlike previous work, our method allows users to quickly identify areas with potential topological errors and to choose how to fix them in a user-friendly manner. Key steps of our algorithm include the conversion of the input model into a set of voxels, the use of morphological operators to allow the user to modify the topology of the discrete model, and the conversion of the corrected voxel set back into a 2-manifold triangle mesh. Our experiments demonstrate that the proposed algorithm is suitable for repairing meshes of a large class of shapes.     

基于特征分析的工程图智能识别算法

提出了一种基于图线和行程段特征分析,由行程段直接拾取整条图线的工程图识别算法。该算法不经过图段分别,根据整条图线的信息确定交点;通过分析线索、间隔等的规律确定线型。最后,基于工程图知识进行图线校正。该算法已付诸实践并取得良好效果。     

3D虚拟仿真技术在体育馆购票系统开发中的应用研究

传统体育馆售票方式无法使购票者在购票时实时感知座位的具体视角。本文提出一种解决思路,其基本思想是根据设计图纸提取场馆的拓扑结构和几何信息,并对场馆三维建模,虚拟仿真体育馆内部场景,再使用OpenGL库函数将模型导入系统来实现场景旋转、转换和购票等人机交互操作。为使用户在购票时更好地感受逼真的三维视角效果,运用画法几何中射线和三角形求交算法解决三维仿真交互中座位定位等问题。     

使用频繁结构提炼网络权威资源

在网络资源中有丰富的、对于许多应用领域有用的动态信息，已有许多的研究工作致力于提高网络中信息检索的质量，然而，这些工作中的大部分仍不能满足用户形形色色的请求．利用网络中的超链接提出新的算法ESFP来改善从搜索引擎返回的搜索结果的质量．运用SFP算法构造ESFP算法，完成从复杂的网络拓扑结构中提取权威的页面和社团．通过运行若干个实验来描述所提出的算法，这些实验数据表明，在处理搜索结果中使用ESFP算法在性能上要优于许多已知的算法如HITS等．     

Map evaluation using matched topology graphs

Mapping is an important task for mobile robots. The assessment of the quality of maps in a simple, efficient and automated way is not trivial and an ongoing research topic. Here, a new approach for the evaluation of 2D grid maps is presented. This structure-based method makes use of a topology graph, i.e., a topological representation that includes abstracted local metric information. It is shown how the topology graph is constructed from a Voronoi diagram that is pruned and simplified such that only high level topological information remains to concentrate on larger, topologically distinctive places. Several methods for computing the similarity of vertices in two topology graphs, i.e., for performing a place-recognition, are presented. Based on the similarities, it is shown how subgraph-isomorphisms can be efficiently computed and two topology graphs can be matched. The match between the graphs is then used to calculate a number of standard map evaluation attributes like coverage, global accuracy, relative accuracy, consistency, and brokenness. Experiments with robot generated maps are used to highlight the capabilities of the proposed approach and to evaluate the performance of the underlying algorithms.     

基于哈希的二维工程 CAD 图纸检索技术

针对企业在设计创新过程中大量采用已有计算机辅助设计(CAD)图纸进行设计重 用的情况,提出了一种基于哈希的二维工程 CAD 图纸检索方法。首先基于环形分割算法提取 工程 CAD 图纸中每个组件对象几何特征;基于传统 LBP 算子提出了一种局部拓扑矢量量化模 式(T-LVQP),实现对各个组件拓扑特征的提取;然后基于协方差描述符融合几何特征和拓扑特 征,通过 LBG 算法将所有组件按照几何特征分组后得到工程 CAD 图纸的特征向量表达;最后 通过迭代量化哈希算法生成图纸的哈希序列。实验结果表明,该算法检索速度快、准确度高, 对于二维工程 CAD 图纸具有较好的检索效果。     

A measurement study supporting P2P file-sharing community models

Knowledge of emergent properties of existing peer-to-peer file-sharing communities can be helpful for the design and implementation of innovative peer-to-peer protocols/services that exploit autonomicity, self-configuration, resilience, scalability, and performance. It is well known that the performance of this class of peer-to-peer applications depends on several parameters that represent the topological structure of the overlay network, the users’ behavior, and resource dynamics. Estimation of these parameters is difficult, but it is crucial for analytical models as well as for realistic simulation of peer-to-peer applications.In this paper, we present an active measurement-based study designed to glean insights on the above parameters within the Gnutella network. The measurement software that we developed is able to collect topological information about the overlay network topology in a few minutes; in a second step, it contacts the users discovered during the topological measurement in order to acquire a novel dataset regarding the shared resources.     

Topology preserving top-down compression of 2D vector fields using bintree and triangular quadtrees

We present a hierarchical top-down refinement algorithm for compressing 2D vector fields that preserves topology. Our approach is to reconstruct the data set using adaptive refinement that considers topology. The algorithms start with little data and subdivide regions that are most likely to reconstruct the original topology of the given data set. We use two different refinement techniques. The first technique uses bintree subdivision and linear interpolation. The second algorithm is driven by triangular quadtree subdivision with Coons patch quadratic interpolation. We employ local error metrics to measure the quality of compression and as a global metric we compute Earth Mover's Distance (EMD) to measure the deviation from the original topology. Experiments with both analytic and simulated data sets are presented. Results indicate that one can obtain significant compression with low errors without losing topological information. Advantages and disadvantages of different topology preserving compression algorithms are also discussed in the paper.     

结合因子图的多目的地地图布局优化

目的提出一种结合因子图的多目的地地图生成方法。方法首先,由用户选择多个感兴趣的目的地,系统根据相应规则自动地选择与目的地最相关的路线。然后,通过定义一组衡量布局质量的约束规则,采用因子图方法将定义的每条规则编码成因子,并采用Metropolis Hastings算法对由因子图构建得到的目标分布函数进行采样得到符合约束规则的多目的地地图。结果实验结果表明,使用这种方法得到的多目的地地图,可以在同一显示空间中显示多个目的地之间的道路信息,同时又保留了各目的地区域之间的拓扑和空间关系。结论提出的多目的地地图能有效地为用户提供导航,解决了当前在线地图无法在同一视野中为用户提供空间距离较远的区域道路信息的问题。     

Improving music recommendation by incorporating social influence

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.