Coherent time-varying graph drawing with multifocus+context interaction

We present a new approach for time-varying graph drawing that achieves both spatiotemporal coherence and multifocus+context visualization in a single framework. Our approach utilizes existing graph layout algorithms to produce the initial graph layout, and formulates the problem of generating coherent time-varying graph visualization with the focus+context capability as a specially tailored deformation optimization problem. We adopt the concept of the super graph to maintain spatiotemporal coherence and further balance the needs for aesthetic quality and dynamic stability when interacting with time-varying graphs through focus+context visualization. Our method is particularly useful for multifocus+context visualization of time-varying graphs where we can preserve the mental map by preventing nodes in the focus from undergoing abrupt changes in size and location in the time sequence. Experiments demonstrate that our method strikes a good balance between maintaining spatiotemporal coherence and accentuating visual foci, thus providing a more engaging viewing experience for the users.     

Visualizing changes of hierarchical data using treemaps

While the treemap is a popular method for visualizing hierarchical data, it is often difficult for users to track layout and attribute changes when the data evolve over time. When viewing the treemaps side by side or back and forth, there exist several problems that can prevent viewers from performing effective comparisons. Those problems include abrupt layout changes, a lack of prominent visual patterns to represent layouts, and a lack of direct contrast to highlight differences. In this paper, we present strategies to visualize changes of hierarchical data using treemaps. A new treemap layout algorithm is presented to reduce abrupt layout changes and produce consistent visual patterns. Techniques are proposed to effectively visualize the difference and contrast between two treemap snapshots in terms of the map items' colors, sizes, and positions. Experimental data show that our algorithm can achieve a good balance in maintaining a treemap's stability, continuity, readability, and average aspect ratio. A software tool is created to compare treemaps and generate the visualizations. User studies show that the users can better understand the changes in the hierarchy and layout, and more quickly notice the color and size differences using our method.     

Route Visualization Using Detail Lenses

We present a method designed to address some limitations of typical route map displays of driving directions. The main goal of our system is to generate a printable version of a route map that shows the overview and detail views of the route within a single, consistent visual frame. Our proposed visualization provides a more intuitive spatial context than a simple list of turns. We present a novel multifocus technique to achieve this goal, where the foci are defined by points of interest (POI) along the route. A detail lens that encapsulates the POI at a finer geospatial scale is created for each focus. The lenses are laid out on the map to avoid occlusion with the route and each other, and to optimally utilize the free space around the route. We define a set of layout metrics to evaluate the quality of a lens layout for a given route map visualization. We compare standard lens layout methods to our proposed method and demonstrate the effectiveness of our method in generating aesthetically pleasing layouts. Finally, we perform a user study to evaluate the effectiveness of our layout choices.     

海量层次信息的Focus+Context交互式可视化技术

综述了海量层次信息可视化与Focus Context技术的相关工作,针对海量层次信息可视化的交互问题,在嵌套圆可视化技术的基础上提出了基于上下文感知的Focus Context交互式可视化技术.首先,基于外切圆排列方法提出对圆心进行三角网格剖分的方法,为变形计算建立上下文;然后,针对变形计算前后上下文一致性问题,在三角网格邻居跟踪方法的基础上,提出了用于同层兄弟节点上下文感知的外切圆变形排列方法,以及用于父子节点上下文感知的嵌套圆迭代排列方法.实验结果表明。上述方法在实现焦点突出的鱼眼视图的同时,能够有效地解决Focus Context交互式可视化的上下文感知问题.上述方法应用于文件系统海量层次信息的交互式可视化问题,提供了交互式可视化工具.     

The use and misuse of focus groups

Focus groups are a somewhat informal technique that can help you assess user needs and feeling both before interface design and long after implementation. In a focus group, you bring together six to nine users to discuss issues and concerns about the features of a user interface. The group typically lasts about two hours and is run by a moderator who maintains the group's focus. Focus groups often bring out users' spontaneous reactions and ideas and let you observe some group dynamics and organizational issues. The paper discusses the use and misuse of focus groups     

Feature-preserving volume data reduction and focus+context visualization

The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.     

Outlier-preserving focus+context visualization in parallel coordinates

Focus+context visualization integrates a visually accentuated representation of selected data items in focus (more details, more opacity, etc.) with a visually deemphasized representation of the rest of the data, i.e., the context. The role of context visualization is to provide an overview of the data for improved user orientation and improved navigation. A good overview comprises the representation of both outliers and trends. Up to now, however, context visualization not really treated outliers sufficiently. In this paper we present a new approach to focus+context visualization in parallel coordinates which is truthful to outliers in the sense that small-scale features are detected before visualization and then treated specially during context visualization. Generally, we present a solution which enables context visualization at several levels of abstraction, both for the representation of outliers and trends. We introduce outlier detection and context generation to parallel coordinates on the basis of a binned data representation. This leads to an output-oriented visualization approach which means that only those parts of the visualization process are executed which actually affect the final rendering. Accordingly, the performance of this solution is much more dependent on the visualization size than on the data size which makes it especially interesting for large datasets. Previous approaches are outperformed, the new solution was successfully applied to datasets with up to 3 million data records and up to 50 dimensions.     

Spatially ordered treemaps

Existing treemap layout algorithms suffer to some extent from poor or inconsistent mappings between data order and visual ordering in their representation, reducing their cognitive plausibility. While attempts have been made to quantify this mismatch, and algorithms proposed to minimize inconsistency, solutions provided tend to concentrate on one-dimensional ordering. We propose extensions to the existing squarified layout algorithm that exploit the two-dimensional arrangement of treemap nodes more effectively. Our proposed spatial squarified layout algorithm provides a more consistent arrangement of nodes while maintaining low aspect ratios. It is suitable for the arrangement of data with a geographic component and can be used to create tessellated cartograms for geovisualization. Locational consistency is measured and visualized and a number of layout algorithms are compared. CIELab color space and displacement vector overlays are used to assess and emphasize the spatial layout of treemap nodes. A case study involving locations of tagged photographs in the Flickr database is described.     

Stereoscopic highlighting: 2D graph visualization on stereo displays

In this paper we present a new technique and prototype graph visualization system, stereoscopic highlighting, to help answer accessibility and adjacency queries when interacting with a node-link diagram. Our technique utilizes stereoscopic depth to highlight regions of interest in a 2D graph by projecting these parts onto a plane closer to the viewpoint of the user. This technique aims to isolate and magnify specific portions of the graph that need to be explored in detail without resorting to other highlighting techniques like color or motion, which can then be reserved to encode other data attributes. This mechanism of stereoscopic highlighting also enables focus+context views by juxtaposing a detailed image of a region of interest with the overall graph, which is visualized at a further depth with correspondingly less detail. In order to validate our technique, we ran a controlled experiment with 16 subjects comparing static visual highlighting to stereoscopic highlighting on 2D and 3D graph layouts for a range of tasks. Our results show that while for most tasks the difference in performance between stereoscopic highlighting alone and static visual highlighting is not statistically significant, users performed better when both highlighting methods were used concurrently. In more complicated tasks, 3D layout with static visual highlighting outperformed 2D layouts with a single highlighting method. However, it did not outperform the 2D layout utilizing both highlighting techniques simultaneously. Based on these results, we conclude that stereoscopic highlighting is a promising technique that can significantly enhance graph visualizations for certain use cases.     

Adapting menu layout to tasks

Menus are an increasingly popular style of user-system interface. Although many aspects of menu design can affect user performance (e.g. item names and selection methods), the organization of items in menus is a particularly salient aspect of their design. Unfortunately, empirical studies of menu layout have yet to resolve the basic question of how menus should be organized to produce optimal performance. Furthermore, a disturbingly common finding has been that any initial effects of menu layout disappear with practice. Thus it is tempting to conclude that menu organization is not important or that it only affects performance during learning. In this paper we present some reasons to doubt this conclusion. In particular, we have found persistent effects of layout with multiple-item selection tasks, in contrast with studies employing a single-item selection paradigm. The results of a controlled study comparing various menu designs (fast-food keyboards) show that the types of tasks to be performed by users must be considered in organizing items in menus and that there may be sustained effects of menu organization with some tasks. In addition, the results of this study support the use of a formal methodology based on user knowledge for menu design. By comparing the performance of subjects using menus designed using our methodology with the performance of subjects using “personalized” menus, we were able to demonstrate the general superiority of our method for designing menus, and for tailoring menus to meet task requirements as well.     

An ensemble method for top-N recommendations from the SVD

Matrix factorization methods such as the singular value decomposition technique have become very popular in the area of recommender systems. Given a rating matrix as input, these techniques output two matrixes with lower dimensional space that represent the user and item features. The relevance of item i to user u is revealed by the score of the dot product between u vector of features and i vector of features. High scores indicate greater relevance. In order to deliver the best recommendations for a given user based on these latent features, one must obtain the list of scores of all the items for the given user and sort the resulting list. When the size of the catalogue is large, this phase consumes a large amount of computational time and cannot be done online. Another drawback with this approach is that once such a list is computed for a given user, it remains finite and it is impossible to incorporate within it new activities of the user. Hence, the use of such techniques is limited online.In this paper we propose an ensemble method for building a forest of trees offline, where each leaf in each tree is holding a unique set of item vectors. Once a user is engaged with the system, its vector is classified to one leaf in each one of the trees in the forest for conducting a dot product with the corresponding items. By using this method we compute online only a small number of dot products for a given user vector allowing us to quickly retrieve dynamic recommendations from the SVD, thereby presenting an alternative to the existing method which computes and caches all of the dot products among the items and users. The method maps the items to the leaves of multiple compact trees offline, each tree is a weak recommendation model, creating a forest of decision trees algorithm in which users that are assigned to these leaves online are likely to produce high dot product scores with the items that are already in the leaves. We demonstrate the effectiveness of the suggested ensemble method by applying it to three public datasets and comparing it to a state-of-the-art algorithm aimed at solving the problem.     

MoleView: an attribute and structure-based semantic lens for large element-based plots

We present MoleView, a novel technique for interactive exploration of multivariate relational data. Given a spatial embedding of the data, in terms of a scatter plot or graph layout, we propose a semantic lens which selects a specific spatial and attribute-related data range. The lens keeps the selected data in focus unchanged and continuously deforms the data out of the selection range in order to maintain the context around the focus. Specific deformations include distance-based repulsion of scatter plot points, deforming straight-line node-link graph drawings, and as varying the simplification degree of bundled edge graph layouts. Using a brushing-based technique, we further show the applicability of our semantic lens for scenarios requiring a complex selection of the zones of interest. Our technique is simple to implement and provides real-time performance on large datasets. We demonstrate our technique with actual data from air and road traffic control, medical imaging, and software comprehension applications.     

Focus+Context Exploration of Hierarchical Embeddings

Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level‐of‐detail‐hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi‐dimensional images.     

一种融合项目特征和移动用户信任关系的推荐算法

协同过滤推荐系统中普遍存在评分数据稀疏问题.传统的协同过滤推荐系统中的余弦、Pearson 等方法都是基于共同评分项目来计算用户间的相似度;而在稀疏的评分数据中,用户间共同评分的项目所占比重较小,不能准确地找到偏好相似的用户,从而影响协同过滤推荐的准确度.为了改变基于共同评分项目的用户相似度计算,使用推土机距离（earth mover's distance,简称EMD）实现跨项目的移动用户相似度计算,提出了一种融合项目特征和移动用户信任关系的协同过滤推荐算法.实验结果表明：与余弦、Pearson 方法相比,融合项目特征的用户相似度计算方法能够缓解评分数据稀疏对协同过滤算法的影响.所提出的推荐算法能够提高移动推荐的准确度.     

LOD map--A visual interface for navigating multiresolution volume visualization

In multiresolution volume visualization, a visual representation of level-of-detail (LOD) quality is important for us to examine, compare, and validate different LOD selection algorithms. While traditional methods rely on ultimate images for quality measurement, we introduce the LOD map--an alternative representation of LOD quality and a visual interface for navigating multiresolution data exploration. Our measure for LOD quality is based on the formulation of entropy from information theory. The measure takes into account the distortion and contribution of multiresolution data blocks. A LOD map is generated through the mapping of key LOD ingredients to a treemap representation. The ordered treemap layout is used for relative stable update of the LOD map when the view or LOD changes. This visual interface not only indicates the quality of LODs in an intuitive way, but also provides immediate suggestions for possible LOD improvement through visually-striking features. It also allows us to compare different views and perform rendering budget control. A set of interactive techniques is proposed to make the LOD adjustment a simple and easy task. We demonstrate the effectiveness and efficiency of our approach on large scientific and medical data sets.     

无监督深度学习模型的多聚焦图像融合算法

深度学习技术应用到多聚焦图像融合领域时,其大多通过监督学习的方式来训练网络,但由于缺乏专用于多聚焦图像融合的监督训练的标记数据集,且制作专用的大规模标记训练集代价过高,所以现有方法多通过在聚焦图像中随机添加高斯模糊进行监督学习,这导致网络训练难度大,很难实现理想的融合效果。为解决以上问题,提出了一种易实现且融合效果好的多聚焦图像融合方法。通过在易获取的无标记数据集上以无监督学习方式训练引入了注意力机制的encoder-decoder网络模型,获得输入源图像的深层特征。再通过形态聚焦检测对获取的特征进行活动水平测量生成初始决策图。运用一致性验证方法对初始决策图优化,得到最终的决策图。融合图像质量在主观视觉和客观指标两方面上进行评定,经实验结果表明,融合图像清晰度高,保有细节丰富且失真度小。     

Visualization and User-Modeling for Browsing Personal Photo Libraries

We present a user-centric system for visualization and layout for content-based image retrieval. Image features (visual and/or semantic) are used to display retrievals as thumbnails in a 2-D spatial layout or “configuration” which conveys all pair-wise mutual similarities. A graphical optimization technique is used to provide maximally uncluttered and informative layouts. Moreover, a novel subspace feature weighting technique can be used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) provided by the user. The resulting retrieval, browsing and visualization can adapt to the user's (time-varying) notions of content, context and preferences in style and interactive navigation. Monte Carlo simulations with machine-generated layouts as well as pilot user studies have demonstrated the ability of this framework to model or “mimic” users, by automatically generating layouts according to their preferences.     

A Movie Recommendation System – An Application of Voting Theory in User Modeling

Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.