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In this paper, we compare two approaches for exploring large,rnhierarchical data spaces of social media data on mobile devicesrnusing facets. While the first approach arranges thernfacets in a 3x3 grid, the second approach makes use of arnscrollable list of facets for exploring the data. We have conductedrna between-group experiment of the two approachesrnwith 24 subjects (20 male, 4 female) executing the same set ofrntasks of typical mobile users" information needs. The resultsrnshow that the grid-based approach requires significantly morernclicks, but subjects need less time for completing the tasks.rnFurthermore, it shows that the additional clicks do not hamperrnthe subjects" satisfaction. Thus, the results suggest thatrnthe grid-based approach is a better choice for faceted searchrnon touchscreen mobile devices. To the best of our knowledge,rnsuch a summative evaluation of different approaches for facetedrnsearch on mobile devices has not been done so far.
Augmented Reality bedeutet eine reale Umgebung mit, meistens grafischen, virtuellen Inhalten zu erweitern. Oft sind dabei die virtuellen Inhalte der Szene jedoch nur ein Overlay und interagieren nicht mit den realen Bestandteilen der Szene. Daraus ergibt sich ein Authentizitätsproblem für Augmented Reatliy Anwendungen. Diese Arbeit betrachtet Augmented Reality in einer speziellen Umgebung, mit deren Hilfe eine authentischere Darstellung möglich ist. Ziel dieserArbeitwar die Erstellung eines Systems, das Zeichnungen durch Techniken der Augmented Reality mit virtuellen Inhalten erweitert. Durch das Anlegen einer Repräsentation soll es der Anwendung dabei möglich sein die virtuellen Szeneelementemit der Zeichnung interagieren zu lassen. Dazu wurden verschiedene Methoden aus den Bereichen des Pose Tracking und der Sketch Recognition disktutiert und für die Implementierung in einem prototypischen System ausgewählt. Als Zielhardware fungiert ein Android Smartphone. Kontext der Zeichnungen ist eine Dungeon Karte, wie sie in Rollenspielen vorkommt. Die virtuellen Inhalte nehmen dabei die Form von Bewohnern des Dungeons an, welche von einer Agentensimulation verwaltet werden. Die Agentensimulation ist Gegenstand einer eigenen Diplomarbeit [18]. Für das Pose Tracking wurde ARToolkitPlus eingesetzt, ein optisches Tracking System, das auf Basis von Markern arbeitet. Die Sketch Recognition ist dafür zuständig die Inhalte der Zeichnung zu erkennen und zu interpretieren. Dafür wurde ein eigener Ansatz implementiert der Techniken aus verschiedenen Sketch Recognition Systemen kombiniert. Die Evaluation konzentriert sich auf die technischen Aspekte des Systems, die für eine authentische Erweiterung der Zeichnung mit virtuellen Inhalten wichtig sind.
Only little information is available about the diffusion of cloud computing in German higher educational institutions. A better understanding of the state of the art in this field would support the modernization of the higher educational institutions in Germany and allow the development of more adequate cloud products and more appropriate business models for this niche. For this purpose, a literature research on Cloud Computing and IT-diffusion will be run and an empirical investigation with an online questionnaire addressed to higher educational institutions in Germany will be performed to illustrate the state of the art of Cloud Computing in German higher educational institutions as well as the threats and opportunities perceived by employees of higher educational institutions data centers connected to the usage of the cloud.
In addition to that, different experts from universities and businesses will be interviewed to complete the knowledge and information collected through the online questionnaire and during the research phase. The expected results will serve to create a recommendation for higher educational institutions in Germany about either they should migration to the cloud or not and introduce a list of guiding questions of critical issues to consider before using cloud-computing technologies.
In this paper, we demonstrate by means of two examples how to work with probability propagation nets (PPNs). The fiirst, which comes from the book by Peng and Reggia [1], is a small example of medical diagnosis. The second one comes from [2]. It is an example of operational risk and is to show how the evidence flow in PPNs gives hints to reduce high losses. In terms of Bayesian networks, both examples contain cycles which are resolved by the conditioning technique [3].
Robotics research today is primarily about enabling autonomous, mobile robots to seamlessly interact with arbitrary, previously unknown environments. One of the most basic problems to be solved in this context is the question of where the robot is, and what the world around it, and in previously visited places looks like " the so-called simultaneous localization and mapping (SLAM) problem. We present a GraphSLAM system, which is a graph-based approach to this problem. This system consists of a frontend and a backend: The frontend- task is to incrementally construct a graph from the sensor data that models the spatial relationship between measurements. These measurements may be contradicting and therefore the graph is inconsistent in general. The backend is responsible for optimizing this graph, i. e. finding a configuration of the nodes that is least contradicting. The nodes represent poses, which do not form a regular vector space due to the contained rotations. We respect this fact by treating them as what they really are mathematically: manifolds. This leads to a very efficient and elegant optimization algorithm.