Technical reports / Department Informatik
Year of publication
- 2013 (4) (remove)
- A Framework for Interactive Physical Simulations on Remote HPC Clusters (2013)
- In this work, we introduce the framework for visualization and interactivity for physics engines in real-time, for short VIPER. It is able to execute various physical simulations, visualize the simulation results in real-time and offer computational steering. Especially interesting in this context are simulations running on remotely accessible HPC clusters. As an example, we present a particulate flow simulation consisting of a coupled rigid body and CFD simulation, the chosen visualization strategy and steering possibilities. Additionally, performance evaluations and a performance prediction model concerning the update rate for remote simulations in the context of the VIPER framework are given.
- A Cost Constrained Boosting Algorithm for Fast Object Detection (2013)
- Boosting methods are among the most widely used machine learning techniques in practice for various reasons. In many scenarios, however, their use is prevented by runtime constraints. In this paper we propose a novel technique for reducing the computational complexity of hierarchical classifiers based on AdaBoost, such as the probabilistic boosting tree, which are often used for object detection. We modify AdaBoost training so that the hypothesis generation is no longer based solely on the weak learner’s training error but also on a measure of hypothesis complexity. This is achieved by incorporating a cost function into the optimization process, effectively constraining feature selection, which leads to a reduced overall classifier complexity and thus shorter evaluation times. The validity of the approach is shown in an experimental valuation on real-world data. In a cross validation experiment with a system for automatic segmentation of liver tumors in CT images, the evaluation cost for classifying previously unseen samples could be reduced by up to 76% using the methods described here without losing classification accuracy.
- Learning a Prior Model for Automatic Liver Lesion Segmentation in Follow-up CT Images (2013)
- Liver tumors that are not surgically removed need to be closely monitored. A common procedure for their assessment involves acquiring CT images every few months and rating disease status based on the largest diameters of a subset of the lesions. The most prominent benefits of automatic lesion segmentation methods in this context are minimization of time consuming interaction and the possibility of volumetric measurements. While existing methods could be applied to each image individually, we propose to incorporate information gained from previous images of the same patient to enhance the segmentation. We learn a Probabilistic Boosting Tree that has an internal representation of tumor growth from a set of training images. Provided a baseline lesion segmentation, it can generate a patient specific lesion prior to guide the segmentation in a follow-up image. In this paper, we describe and compare different methods for building the growth model and integrating it into a segmentation system. The validity of the approach is shown in an experimental evaluation on a database of 14 patients. On the 17 pairs of baseline and follow-up images in this database, segmentation performance was measured once without and once with the proposed prior. When comparing the points of 90% sensitivity from each experiment, introducing the prior improved the precision of the segmentation from 82.7% to 91.9%. This corresponds to a reduction of the number of false positive voxels per true positive voxel by 57.8%.
- Usability vs. Security: The Everlasting Trade-Off in the Context of Apple iOS Mobile Hotspots (2013)
- Passwords have to be secure and usable at the same time, a trade-off that is long known. There are many approaches to avoid this trade-off, e.g., to advice users on generating strong passwords and to reject user passwords that are weak. The same usability/security trade-off arises in scenarios where passwords are generated by machines but exchanged by humans, as is the case in pre-shared key (PSK) authentication. We investigate this trade-off by analyzing the PSK authentication method used by Apple iOS to set up a secure WPA2 connection when using an iPhone as a Wi-Fi mobile hotspot. We show that Apple iOS generates weak default passwords which makes the mobile hotspot feature of Apple iOS susceptible to brute force attacks on the WPA2 handshake. More precisely, we observed that the generation of default passwords is based on a word list, of which only 1.842 entries are taken into consideration. In addition, the process of selecting words from that word list is not random at all, resulting in a skewed frequency distribution and the possibility to compromise a hotspot connection in less than 50 seconds. Spot tests show that other mobile platforms are also affected by similar problems. We conclude that more care should be taken to create secure passwords even in PSK scenarios.