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The breaking of solid objects, like glass or pottery, poses a complex problem for computer animation. We present our methods of using physical simulation to drive the animation of breaking objects. Breakage is obtained in a three-dimensional flexible model as the limit of elastic behavior. This article describes three principal features of the model: a breakage model, a collision-detection/response scheme, and a geometric modeling method. We use networks of point masses connected by springs to represent physical objects that can bend and break. We present efficient collision-detection algorithms, appropriate for simulating the collisions between the various pieces that interact in breakage. The capability of modeling real objects is provided by a technique of building up composite structures from simple lattice models. We applied these methods to animate the breaking of a teapot and other dishware activities in the animation Tipsy Turvy shown at Siggraph '89. Animation techniques that rely on physical simulation to control the motion of objects are discussed, and further topics for research are presented.

In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.

The last decade has seen a rapid growth in both the volume and variety of network traffic, while at the same time, the need to analyze the traffic for quality of service, security, and misuse has become increasingly important. In this paper, we will present a traffic analysis system that couples visual analysis with a declarative knowledge representation based on first order logic. Our system supports multiple iterations of the sense-making loop of analytic reasoning, by allowing users to save their discoveries as they are found and to reuse them in future iterations. We will show how the knowledge base can be used to improve both the visual representations and the basic analytical tasks of filtering and changing level of detail. More fundamentally, the knowledge representation can be used to classify the traffic. We will present the results of applying the system to successfully classify 80% of network traffic from one day in our laboratory. INTRODUCTIONThe last decade has seen a rapid growth in both the volume and variety of network traffic, while at the same time it is becoming ever more important for analysts to understand network behaviors to provide quality of service, security, and misuse monitoring. To aid analysts in these tasks, researchers have proposed numerous visualization techniques that apply exploratory analysis to network traffic. The sense-making loop of information visualization is critical for analysis [5]. The loop involves a repeated sequence of hypothesis, experiment, and discovery. However, current visual analysis systems for network traffic do not support sense-making well because they provide no means for analysts to save their discoveries and build upon them. As such, it becomes the analyst's burden to remember and reason about the multitude of patterns observed during visual analysis, which quickly becomes impossible in massive datasets typical of network traffic.In this paper we present a network traffic visualization system that enables previous visual discoveries to be used in future analysis. The system accomplishes this by allowing the analyst to interactively create logical models of the visual discoveries. The logical models are stored in a knowledge representation and can be reused. The reuse of knowledge creates an analytical cycle as summarized in figure 1. In addition to facilitating the sensemaking loop, knowledge representations allow the creation of more insightful visualizations that the analyst can use to discover more complex and subtle patterns.To evaluate effectiveness, we will present the results of applying our system to analyze one day of network traffic from our laboratory. This paper will be structured as follows: section 2 will provide an overview of the visual analysis process; section 3 will give a sampling of related work in this area; section 4 will describe the system's knowledge representation; section 5 will overview the visual knowledge creation; section 6 will demonstrate how the system leverages the knowledge base to improve visual analysis...

Comparing slopes is a fundamental graph reading task and the aspect ratio chosen for a plot influences how easy these comparisons are to make. According to Banking to 45°, a classic design guideline first proposed and studied by Cleveland et al., aspect ratios that center slopes around 45° minimize errors in visual judgments of slope ratios. This paper revisits this earlier work. Through exploratory pilot studies that expand Cleveland et al.'s experimental design, we develop an empirical model of slope ratio estimation that fits more extreme slope ratio judgments and two common slope ratio estimation strategies. We then run two experiments to validate our model. In the first, we show that our model fits more generally than the one proposed by Cleveland et al. and we find that, in general, slope ratio errors are not minimized around 45°. In the second experiment, we explore a novel hypothesis raised by our model: that visible baselines can substantially mitigate errors made in slope judgments. We conclude with an application of our model to aspect ratio selection.