Figure 1: FDIVE learns to distinguish relevant from irrelevant data through an iteratively improving classification model by learning the best-fitting feature descriptor and distance function. (1) Users express their notion of relevance by labeling a set of query items, in this case, images. (2) These labels are used to rank all similarity measures by their ability to distinguish relevant from irrelevant data. (3) The system applies the selected similarity measure to learn a Self-Organizing Map (SOM)-based relevance model. Users explore and refine the model by supplying relevance labels in uncertain data regions, especially near the decision boundaries. ABSTRACTThe detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDIVE, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDIVE enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.

<div>The automated analysis of digital human communication data often focuses on specific aspects like content or network structure in isolation, while classical communication research stresses the importance of a holistic analysis approach. This work aims to formalize digital communication analysis and investigate how classical results can be leveraged as part of visually interactive systems, which offers new analysis opportunities to allow for less biased, skewed, or incomplete results. For this, we construct a conceptual framework and design space based on the existing research landscape, technical considerations, and communication research that describes the properties, capabilities, and composition of such systems through 30 criteria in four analysis dimensions. We make the case how visual analytics principles are uniquely suited for a more holistic approach by tackling the automation complexity and leverage domain knowledge, paving the way to generate design guidelines for building such approaches. Our framework provides a common language and description of communication analysis systems to support existing research, highlights relevant design areas while promoting and supporting the mutual exchange between researchers. Additionally, our framework identifies existing gaps and highlights opportunities in research areas that are worth investigating further. With this contribution, we pave the path for the formalization of digital communication analysis through visual analytics.</div>

Historical change typically is the result of complex interactions between several linguistic factors. Identifying the relevant factors and understanding how they interact across the temporal dimension is the core remit of historical linguistics. With respect to corpus work, this entails a separate annotation, extraction and painstaking pair-wise comparison of the relevant bits of information. This paper presents a significant extension of HistoBankVis, a multilayer visualization system which allows a fast and interactive exploration of complex linguistic data. Linguistic factors can be understood as data dimensions which show complex interrelationships. We model these relationships with the Parallel Sets technique. We demonstrate the powerful potential of this technique by applying the system to understanding the interaction of case, grammatical relations and word order in the history of Icelandic.

a) Regular rendering (b) Slope-dependent rendering (c) Regular rendering (d) Slope-dependent rendering Figure 1: Comparison of regular parallel coordinates with our slope-dependent polyline rendering. Parallel coordinates face two problems, which are inherent in the technique: (a) depicts three clusters of the same diameter and size across all dimensions. Diagonal changes of the clusters are visually more prominent, as diagonal lines are rendered more closely. (c) shows 200 data points of uniform random clutter/noise in all dimensions. Zig-zag clusters are visible as diagonal lines and are perceived as clusters, although there are no such clusters in the data (ghost clusters). We propose to render each line segment based on its slope between two axes. As a result, clusters are not distorted by their shape (b), and the ghost clusters effect is reduced (d). ABSTRACTParallel coordinates are a popular technique to visualize multidimensional data. However, they face a significant problem influencing the perception and interpretation of patterns. The distance between two parallel lines differs based on their slope. Vertical lines are rendered longer and closer to each other than horizontal lines. This problem is inherent in the technique and has two main consequences: (1) clusters which have a steep slope between two axes are visually more prominent than horizontal clusters.(2) Noise and clutter can be perceived as clusters, as a few parallel vertical lines visually emerge as a ghost cluster. Our paper makes two contributions: First, we formalize the problem and show its impact. Second, we present a novel technique to reduce the effects by rendering the polylines of the parallel coordinates based on their slope: horizontal lines are rendered with the default width, lines with a steep slope with a thinner line. Our technique avoids density distortions of clusters, can be computed in linear time, and can be added on top of most parallel coordinate variations. To demonstrate the usefulness, we show examples and compare them to the classical rendering.

Figure 1: VULNEX is a tool for the investigation of exposure to open-source software vulnerabilities on an organization-wide level.The tool shows repositories, modules, libraries, vulnerabilities in a tree representation (A), and meta-information about each entry (B), such as the CVSS score. We can see that the "low-marmoset" repository is exposed to severe vulnerabilities, three critical and seven high. Two of the critical vulnerabilities are originating from the activemq-all indicating that the library should be updated swiftly.