Several studies have shown that users of immersive virtual reality can feel high levels of embodiment in self avatars that have different morphological proportions th a n th ose of their actual ...bodies . Deformed and unrealistic morphological modifications are accepted by embodied users, underlying the adaptability of one's mental map of their body (body schema) in response to incomi ng sensory feedback. Before initiating a motor action, the brain uses the body schema to plan and sequence the necessary movements. Therefore, embodiment in a self avatar with a different mo rphology, such as one with deformed proportions, could lead to changes in motor planning and execution. In this study, we aimed to measure the effects on movement planning and execution of embodying a self avatar with an enlarged lower leg on one side. Thirty participants embodied an avatar without any deformations, and with an enlarged dominant or non domin ant leg, in randomized order. Two different levels of embodiment were induced, using synchronous or asynchronous visuotactile stimuli. In each condition , participants performed a gait initiation task. Their center of mass and center of pressure were measured, and the margin of stability (MoS) was computed from these values. Their perceived level of embodiment was also measured, using a validated questionnaire. Results show no significant changes on the biom e chenical variables related to dynamic stability Embodiment scores decreased with asynchronous stimuli, without impacting the measures related to stability. The body schema may not have been impacted by the larger virtual leg . Ho wever, d eforming the self avatar's morphology could have important implications when addressing individuals with impaired physical mobility by subtly influencing action execution dur ing a rehabilitation protocol.
We present Lyra, an interactive environment for designing customized visualizations without writing code. Using drag‐and‐drop interactions, designers can bind data to the properties of graphical ...marks to author expressive visualization designs. Marks can be moved, rotated and resized using handles; relatively positioned using connectors; and parameterized by data fields using property drop zones. Lyra also provides a data pipeline interface for iterative, visual specification of data transformations and layout algorithms. Visualizations created with Lyra are represented as specifications in Vega, a declarative visualization grammar that enables sharing and reuse. We evaluate Lyra's expressivity and accessibility through diverse examples and studies with journalists and visualization designers. We find that Lyra enables users to rapidly develop customized visualizations, covering a design space comparable to existing programming‐based tools.
Data visualization is now a popular medium for journalistic storytelling. However, current visualization tools either lack support for storytelling or require significant technical expertise. ...Informed by interviews with journalists, we introduce a model of storytelling ions that includes state‐based scene structure, dynamic annotations and decoupled coordination of multiple visualization components. We instantiate our model in Ellipsis: a system that combines a domain‐specific language (DSL) for storytelling with a graphical interface for story authoring. User interactions are automatically translated into statements in the Ellipsis DSL. By enabling storytelling without programming, the Ellipsis interface lowers the threshold for authoring narrative visualizations. We evaluate Ellipsis through example applications and user studies with award‐winning journalists. Study participants find Ellipsis to be a valuable prototyping tool that can empower journalists in the creation of interactive narratives.
We introduce MultiPiles, a visualization to explore time‐series of dense, weighted networks. MultiPiles is based on the physical analogy of piling adjacency matrices, each one representing a single ...temporal snapshot. Common interfaces for visualizing dynamic networks use techniques such as: flipping/animation; small multiples; or summary views in isolation. Our proposed ‘piling’ metaphor presents a hybrid of these techniques, leveraging each one's advantages, as well as offering the ability to scale to networks with hundreds of temporal snapshots. While the MultiPiles technique is applicable to many domains, our prototype was initially designed to help neuroscientists investigate changes in brain connectivity networks over several hundred snapshots. The piling metaphor and associated interaction and visual encodings allowed neuroscientists to explore their data, prior to a statistical analysis. They detected high‐level temporal patterns in individual networks and this helped them to formulate and reject several hypotheses.
We introduce an algorithm for automatic selection of semantically‐resonant colors to represent data (e.g., using blue for data about “oceans”, or pink for “love”). Given a set of categorical values ...and a target color palette, our algorithm matches each data value with a unique color. Values are mapped to colors by collecting representative images, analyzing image color distributions to determine value‐color affinity scores, and choosing an optimal assignment. Our affinity score balances the probability of a color with how well it discriminates among data values. A controlled study shows that expert‐chosen semantically‐resonant colors improve speed on chart reading tasks compared to a standard palette, and that our algorithm selects colors that lead to similar gains. A second study verifies that our algorithm effectively selects colors across a variety of data categories.
The field of cyber security is faced with ever‐expanding amounts of data and a constant barrage of cyber attacks. Within this space, we have designed BubbleNet as a cyber security dashboard to help ...network analysts identify and summarize patterns within the data. This design study faced a range of interesting constraints from limited time with various expert users and working with users beyond the network analyst, such as network managers. To overcome these constraints, the design study employed a user‐centered design process and a variety of methods to incorporate user feedback throughout the design of BubbleNet. This approach resulted in a successfully evaluated dashboard with users and further deployments of these ideas in both research and operational environments. By explaining these methods and the process, it can benefit future visualization designers to help overcome similar challenges in cyber security or alternative domains.
Today it is quite common for people to exchange hundreds of comments in online conversations (e.g., blogs). Often, it can be very difficult to analyze and gain insights from such long conversations. ...To address this problem, we present a visual text analytic system that tightly integrates interactive visualization with novel text mining and summarization techniques to fulfill information needs of users in exploring conversations. At first, we perform a user requirement analysis for the domain of blog conversations to derive a set of design principles. Following these principles, we present an interface that visualizes a combination of various metadata and textual analysis results, supporting the user to interactively explore the blog conversations. We conclude with an informal user evaluation, which provides anecdotal evidence about the effectiveness of our system and directions for further design.
The State-of-the-Art of Set Visualization Alsallakh, Bilal; Micallef, Luana; Aigner, Wolfgang ...
Computer graphics forum,
February 2016, Letnik:
35, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same ...collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net.
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non‐trivial problem due to the large number of possible relations between them. We provide a systematic overview of state‐of‐the‐art techniques for visualizing different kinds of set relations.We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem.
Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces ...additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data.
We present a system to analyze time‐series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo‐referenced sensor data, in particular for anomaly ...detection. We split the recordings into fixed‐length patterns and show them in order to compare them over time and space using two linked views. Apart from geo‐based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities.
The methods we use are best practices in the information visualization domain. They cover the daily, the weekly and seasonal and patterns of the data. Daily patterns can be analyzed in a clustering‐based view, weekly patterns in a calendar‐based view and seasonal patters in a projection‐based view. The connectivity of the sensors can be analyzed through a dedicated topological network view. We assist the domain expert with interaction techniques to make the results understandable. As a result, the user can identify and analyze erroneous and suspicious measurements in the network. A case study with a domain expert verified the usefulness of our approach.
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