The advancement of biotechnology enabled the development of "biotic video games", where human players manipulate real biological samples for fun and educational human-biology interactions. However, new design principles are needed to both leverage and mitigate biological properties (e.g., variability and stochasticity), and create unique play experiences that transcend traditional video games. This paper describes the implementation of Pac-Euglena, a biotic Pac-Man analog, where players guide live microscopic Euglena cells with light stimuli through a physical microfluidic maze. Through use of multi-modal stimuli, a mixed biology-digital-human reality is achieved, enabling cell interactions with virtual ghosts and collectibles. Through an iterative design process, we illustrate challenges and strategies for designing games with living organisms. A user study (n=18, conducted at a university event) showed that Pac-Euglena was fun, stimulated curiosity, and taught users about Euglena. We conclude with five general guidelines for the design and development of biotic games and HBI interfaces.

Raster clip-art images, which consist of distinctly colored regions separated by sharp boundaries typically allow for a clear mental vector interpretation. Converting these images into vector format can facilitate compact lossless storage and enable numerous processing operations. Despite recent progress, existing vectorization methods that target such data frequently produce vectorizations that fail to meet viewer expectations. We present PolyFit , a new clip-art vectorization method that produces vectorizations well aligned with human preferences. Since segmentation of such inputs into regions had been addressed successfully, we specifically focus on fitting piecewise smooth vector curves to the raster input region boundaries, a task prior methods are particularly prone to fail on. While perceptual studies suggest the criteria humans are likely to use during mental boundary vectorization, they provide no guidance as to the exact interaction between them; learning these interactions directly is problematic due to the large size of the solution space. To obtain the desired solution, we first approximate the raster region boundaries with coarse intermediate polygons leveraging a combination of perceptual cues with observations from studies of human preferences. We then use these intermediate polygons as auxiliary inputs for computing piecewise smooth vectorizations of raster inputs. We define a finite set of potential polygon to curve primitive maps, and learn the mapping from the polygons to their best fitting primitive configurations from human annotations, arriving at a compact set of local raster and polygon properties whose combinations reliably predict human-expected primitive choices. We use these primitives to obtain a final globally consistent spline vectorization. Extensive comparative user studies show that our method outperforms state-of-the-art approaches on a wide range of data, where our results are preferred three times as often as those of the closest competitor across multiple types of inputs with various resolutions.

It is hard for security practitioners and decision-makers to know what level of protection they are getting from their investments in security, especially when they have invested in a number of technologies and processes which interact and combine together. It is even harder to estimate how well these investments can be expected to protect their organizations in the future as security policies, regulations and the threat environment are constantly changing. In this paper we propose that for measuring the effectiveness of security processes in large organizations, a greater emphasis needs to be put on process-based metrics, in contrast to the more commonly used symptomatic lagging indicators. We show, by means of two case studies, how these process-based metrics can be combined with executable, predictive models, based on a sound mathematical foundation, to both assess organizations' security processes under current conditions and predict how well they are likely to perform in potential future scenarios, which may include changes in working practices, policies or threat levels, or new investments in security. We present two case studies, in the areas of vulnerability threat management, and identity and access management, as significant examples to illustrate how this modeling and simulation-based approach can be used to provide a rich picture of how well existing security processes are protecting the organization and to answer "what-if" questions, such as exploring the effects of a change in security policy or an investment in new security technology. Our approach enables the organization to apply the metrics that are most relevant to its business, and provide a comprehensive view that shows the benefits and losses to the different stakeholders.

In this paper we present a novel approach of using mathematical models and stochastic simulations to guide and inform security investment and policy change decisions. In particular, we investigate vulnerability management policies, and explore how effective standard patch management and emergency escalation based policies are, and how they can be combined with earlier, pre-patch mitigation measures to reduce the potential exposure window. The paper describes the model we constructed to represent typical vulnerability management processes in large organizations, which captures the external threat environment and the internal security processes and decision points. We also present the results from the experimental simulations, and show how changes in security solutions and policies, such as speeding up patch deployment and investing in early mitigation measures, affect the overall exposure window in terms of the time it takes to reduce the potential risk. We believe that this type of mathematical modelling and simulation-based approach provides a novel and useful way of considering security investment decisions, which is quite distinct from traditional risk analysis.