University of Passau

About: University of Passau is a education organization based out in Passau, Bayern, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 1543 authors who have published 4763 publications receiving 93338 citations.

Collaborative Crowd Density Estimation with Mobile Phones

Abstract: We present a technique for estimating crowd density by using a mobile phone to scan the environment for discoverable Bluetooth devices. The paper builds on previous work directed to using Bluetooth scans to analyse social context and extends it with more advanced features, leveraging collaboration between several close by devices, and the use of relative features that do not directly depend on the absolute number of devices in the environment. The method is evaluated on an extensive data set from a three day experiment at the Munich Octoberfest festival showing over 80% recognition accuracy (on four discrete crowd density classes) with 30% improvement over the simple method of just counting discoverable devices investigated in previous work.

Faster discovery of faster system configurations with spectral learning

Abstract: Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT's innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors--less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2---10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.

Robust, low cost indoor positioning using magnetic resonant coupling

Abstract: We describe the design, implementation, and evaluation of an indoor positioning system based on resonant magnetic coupling. The system has an accuracy of less than 1 m2 and, because of the underlying physical principle, is robust with respect to disturbances such as people moving around or changes in room configuration. It consists of 16x16x16 cm transmitter coils, each able to cover an area of up to 50 m2, and provides location information to an arbitrary number of mobile receivers with an update rate of up to 30Hz. We evaluate the actual accuracy of the positioning with a robotic arm and show quantitatively that even large metallic objects have little effect on the signal. We then present an elaborate study of the performance of our system for the recognition of abstract locations such as "at the table", "in front of a cabinet". It comprises four different sites with a total of 100 individual locations some as little as 50 cm apart.

Efficient path conditions in dependence graphs

Abstract: Program slicing combined with constraint solving is a powerful tool for software analysis. Path conditions are generated for a slice or chop, which --- when solved for the input variables --- deliver compact "witnesses" for dependences or illegal influences between program points.In this contribution we show how to make path conditions work for large programs. Aggressive engineering, based on interval analysis and BDDs, is shown to overcome the potential combinatoric explosion. Case studies and empirical data will demonstrate the usefulness of path conditions for practical program analysis.

Revisiting the relationship between fault detection, test adequacy criteria, and test set size

Abstract: The research community has long recognized a complex interrelationship between fault detection, test adequacy criteria, and test set size. However, there is substantial confusion about whether and how to experimentally control for test set size when assessing how well an adequacy criterion is correlated with fault detection and when comparing test adequacy criteria. Resolving the confusion, this paper makes the following contributions: (1) A review of contradictory analyses of the relationships between fault detection, test adequacy criteria, and test set size. Specifically, this paper addresses the supposed contradiction of prior work and explains why test set size is neither a confounding variable, as previously suggested, nor an independent variable that should be experimentally manipulated. (2) An explication and discussion of the experimental designs of prior work, together with a discussion of conceptual and statistical problems, as well as specific guidelines for future work. (3) A methodology for comparing test adequacy criteria on an equal basis, which accounts for test set size without directly manipulating it through unrealistic stratification. (4) An empirical evaluation that compares the effectiveness of coverage-based testing, mutation-based testing, and random testing. Additionally, this paper proposes probabilistic coupling, a methodology for assessing the representativeness of a set of test goals for a given fault and for approximating the fault-detection probability of adequate test sets.