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visual exploration of Differences Among DTI Fiber Models

Chinese Journal of Electronics 2018年第5期27卷 959-967页

作者： MEI Honghui GUO Fangzhou CHEN Haidong CHEN Yi CHEN Wei State Key Lab of CAD&CG Zhejiang University Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University

In-vivo studies of fibrous structures require non-invasive tools, of which one is fiber tracking based on Diffusion tensor imaging(DTI) datasets. Different fiber models can be produced from different DTI images, which may vary from subject to subject due to variations in anatomy, motions in scanning, and signal noises. Additionally, parameters of the tracking method also have a great influence on resulting models. Illustrating, exploring, and analyzing differences among DTI fiber models are crucial for the purposes of group comparison,atlas construction, and uncertainty analysis. Conventional approaches illustrate fiber models in 3D space and explore differences either voxel-wisely or fiber-based. However, these approaches rely on accurate alignment processes and may easily be disturbed by visual clutters. We introduce a two-phase pro jection technique to illustrate a complex 3D fiber model with a unique 2D map to characterize features for further exploration and analysis. Moreover, regions of significant differences among the maps are marked out. In these 2D maps, differences can be easily distinguished without occlusions that often occur in 3D spaces. To facilitate comparative analysis from multiple perspectives, we design an interface for interactive exploration. The effectiveness of our approach is evaluated with two datasets.

关键词： Diffusion tensor imaging Fiber tracking Difference visualization visual exploration

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JobViz:Skill-driven visual exploration of job advertisements

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visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： visual exploration Job advertisements Skill-driven

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Multidimensional Projections for visual Analysis of Social Networks

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Journal of Computer Science & Technology 2012年第4期27卷 791-810页

作者： Rafael Messias Martins Gabriel Faria Andery Henry Heberle Fernando Vieira Paulovich Alneu de Andrade Lopes Helio Pedrini Rosane Minghim Institute of Mathematics and Computer Science University of Sao PauloSao Carlos 13566-590Brazil CPM Braxis Capgemini Sao Paulo 04543-900Brazil Institute of Computing University of CampinasCampinas-SP 13083-852Brazil

visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in atgribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.

关键词： social network visual exploration multidimensional visualization

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A Tag-based way of organizing and exploring literature resources in Web 2.0 environment

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Chinese Journal of Library and Information Science 2008年第1期1卷 52-64页

作者： ZHANG Ming FENG Tianxiao HUANG Yanjing School of Electronics Engineering & Computer Science Peking University

With the rapid development of Web2.0 technology, more and more social annotation systems are emerging, such as ***, Flickr, YouTube, and CiteULike. These systems help users to manage and share their digital resources, and have attracted a lot of users to annotate the resources with tags and bookmarks, which result in a large scale of tag data. Due to the exponential increase of social annotations, all the users are facing the same problem: How can we explore the desired resources efficiently in such a large tag dataset? Since the traditional methods such as tag cloud view and annotation match work well only in small annotation dataset, this paper studies the relationships of tag-tag, tag-resource and resource-resource through the co-occurrences and proposes a new efficient way for users to organize and explore the literature resources. Our research mainly focuses on two aspects:1) The hidden semantic relationships of popular tags and their relevant literature resources;2) the computing of literature resources similarity given a specific literature. A prototype system named PKUSpace is implemented and shows promising results.

关键词： Web2.0 Tag Social annotation tool Semantic relationship visual exploration

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