Oliver Brdiczka

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Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context/tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing(More)
This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is(More)
The annual incidence of insider attacks continues to grow, and there are indications this trend will continue. While there are a number of existing tools that can accurately identify known attacks, these are reactive (as opposed to proactive) in their enforcement, and may be eluded by previously unseen, adversarial behaviors. This paper proposes an approach(More)
In a study of notetaking in university courses, we found that the large majority of students prefer the paper medium to the computer for taking notes and making annotations. Based on this finding, we developed CoScribe, a system which supports students in making collaborative handwritten annotations on printed lecture slides. It includes mechanisms for the(More)
This paper introduces a Bayesian network model for the motivation and psychology of the malicious insider. First, an initial model was developed based on results in the research literature, highlighting critical variables for the prediction of degree of interest in a potentially malicious insider. Second, a survey was conducted to measure these predictive(More)
Using a networked infrastructure of easily available sensors and context-processing components, we are developing applications for the support of workplace interactions. Notions of activity and availability are learned from labeled sensor data based on a Bayesian approach. The higher-level information on the users is then automatically derived from(More)
This paper presents CoScribe, a concept and prototype system for the combined work with printed and digital documents, which supports a large variety of knowledge work settings. It integrates novel pen-and-paper-based interaction techniques that enable users to collaboratively annotate, link and tag both printed and digital documents. CoScribe provides for(More)
Email is a ubiquitous communication tool and constitutes a significant portion of social interactions. In this paper, we attempt to infer the personality of users based on the content of their emails. Such inference can enable valuable applications such as better personalization, recommendation, and targeted advertising. Considering the private and(More)