In early game development phases game designers adjust game rules in a rapid, iterative and flexible way. In later phases, when software prototypes are available, play testing provides more detailed feedback about player experience. More often than not, the realized and the intended gameplay emerging from game software differ. Unfortunately, adjusting it is hard because designers lack a means for efficiently defining, fine-tuning and balancing game mechanics. The language Machinations provides a graphical notation for expressing the rules of game economies that fits with a designer's understanding and vocabulary, but is limited to design itself. Micro-Machinations (MM) formalizes the meaning of core language elements of Machinations enabling reasoning about alternative behaviors and assessing quality, making it also suitable for software development. We propose an approach for designing, embedding and adapting game mechanics iteratively in game software, and demonstrate how the game mechanics and the gameplay of a tower defense game can be easily changed and promptly play tested. The approach shows that MM enables the adaptability needed to reduce design iteration times, consequently increasing opportunities for quality improvements and reuse.
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MSEs have encountered limitations while pushing the limits of catheter tip sensors performance. The limitations summarized: - sensors are not immune to electrical signal noise, cross talk, and EM fields; - sensors are not immune to high magnetic fields, i.e. not suitable for MR imaging; - extending the amount of sensors on the catheter tip is limited due to cluttering of wires. A fundamentally different approach using integrated optics is chosen for developing a new generation catheter sensors. The complexity of the design and production problems represents a knowledge gap, that can be bridged in the proposed consortium. This project consists of four work packages, total duration two years, subdivided into four phases. A crucial deliverable of the project is presented at the end of phase IV (WP4), namely a demonstrator integrating pressure and temperature sensors (obtained from WP1) with a newly designed readout system. This system is modularly extendable for future catheter tip sensors. In WP1, pressure- and temperature sensors are developed using two design approaches. In WP2 the influence of downscaling an ultrasound MZI device is explored and the microfabrication process parameters are studied. An additional goal of WP2 is to find the most suitable method for measuring lactate concentration. Among the deliverables five manuscripts: manuscript 1 includes simulations and measurements of the developed pressure and temperature sensors, manuscript 2 answers the question: can a grated fiber be used for measuring pressure and temperature on a tip? Manuscript 3 answers the question: which method is most suitable for measuring lactate concentration on a tip? Manuscript 4 answers the question: does a US intensity detector fit on a catheter tip while obeying the LoR? Manuscript 5 describes the performance of the demonstrator (Phase IV), i.e. integration of T/P sensing with a modular read out system.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
In this project I conduct a combination of both artistic and design research that produces an intervention in the domains of Photography and BioArt. BioArt is an art practice where artists work and/or collaborate with living organisms and life processes to create works of art and/or artistic processes. This research connects to the description of the ‘Arts + Creative PD programme’ on the basis that this – again: both artistic as well as design – research responds to a question arisen from the very centre of my own creative practice, but that its relevance focuses on an intervention in specific social-political contexts. Further detail on this can be found in the ‘Praktijkprobleem’-section and in the ‘Domeinspecifieke onderdelen’- section of this proposal. The research is grounded in a combining of artistic, design and scientific (microbiological) methodologies, resulting in an intervention not only into the practice, but also in the ethical foundations of the domains of Photography and BioArt.
