The cueTable Cooperative Multi-Touch Interactive Tabletop: Implementation and User Feedback

Es wurde ein multi-touch interaktives Tabletop als Basistechnologie zur Exploration neuer Interaktionskonzepte für kooperative multi-touch Anwendungen entwickelt. In dieser Publikation stellen wir vor, wie ein kooperatives multi-touch interaktives Tabletop basierend auf günstiger Standard-Hardware mit geringem Realisierungsaufwand gebaut werden kann. Wir präsentieren eine Software-Anwendung, die wir dafür entwickelt haben. And wir berichten über Benutzerkommentare zum Tabletop und der Anwendung.We developed a multi-touch interactive tabletop as a base technology to explore new interaction concepts for cooperative multi-touch applications. In this paper we explain how to build a cooperative multi-touch interactive tabletop with standard and low-budget hardware and little implementation effort. We present a software application we developed. And we report on user feedback to the tabletop and the application

CoDaMine: Supporting Privacy and Trust Management in Ubiquitous Environments Through Communication Data Mining

In ubiquitous environments an increasing number of sensors capture information on users and at the same time an increasing number of actuators are available to present information to users. This vast capturing of information potentially enables the system to adapt to the users. At the same time the system might violate the users' privacy by capturing information that the users do not want to share, and the system might disrupt the users by being too obtrusive in its adaptation or information supply. In this paper we present CoDaMine - a novel approach for providing users with system - generated feedback and control in ubiquitous environments giving them the freedom they need while reducing their effort. Basically, CoDaMine captures and analyses the users' online communication to learn about their social relationships in order to provide them with recommendations for inter-personal privacy and trust management

New Concepts for Presence and Availability in Ubiquitous and Mobile Computing - Enabling Selective Availability through Stream-Based Active Learning

Modern Computer-mediated Communication technologies like Instant Messaging (IM) systems enable spontaneous communication over distance. With the advances in Mobile and Ubiquitous Computing, these technologies move away from the desktop computers of our offices, and become more and more pervasive and interwoven with our daily lives. The introduction of these great possibilities to communicate from everywhere with everyone however comes at a cost: The cost of constantly being available to everybody, everywhere, leading to an increasing number of interruptions in our daily tasks. The challenge is, that current technology does not empower users to manage their availability in an adequate manner. Most IM clients for example, only support one single online status that needs to be managed manually by the user. In this work I am founding the concepts of Presence and Availability on a deep understanding of human privacy needs, derived from literature. Based on this foundation, I show how the selective and dynamic nature of privacy is not sufficiently reflected in current systems. Based on two user studies I reveal patterns for selective information disclosure and present an analysis of Selective Availability needs. With the collected study data, I further show that Selective Availability for nomadic users can be predicted based on sensors installed on the users’ laptop computer with a good accuracy through machine learning. As the personalised nature of the data requires new concepts for building an adaptive system, I introduce the LILOLE Framework. The LILOLE Framework outlines the concept of an adaptive system that relies on stream-based active learning to continuously learn and automatically adapt fine-grained personal availability preferences for individual users. The concept is validated through a proof-of-concept implementation and an evaluation based on real user data. In comparison to related work, the presented work is one of very few examples that goes beyond the pure analysis of the predictability, but provides a concept and an implementation of a real system as validation. My approach is novel by combining concepts from Data Stream Mining and Active Learning to predict availability, thus making it very flexible for different settings. This way I am able to address the selective and dynamic nature of availability preferences for nomadic users

Disclosure Templates for Selective Information Disclosure

Cooperative environments often capture data about users and mutually inform users in order to facilitate coordination in distributed workgroups. For the users this entails benefits, but also challenges regarding privacy. In this paper we introduce the concept of Disclosure Templates to help users configure environments according to their privacy needs. They provide powerful configuration while keeping users ’ effort low. These Disclosure Templates have been derived from a literature study and an empirical study, and they have been implemented in the PRIMIFaces environment