We propose a new method to fill missing or invalid values in depth images generated from the Kinect depth sensor. To fill the missing depth values, we use a robust least median of squares (LMedS) approach. We apply our method for telepresence environments, where Kinects are used very often for reconstructing the captured scene in 3D. We introduce a modified 1D LMedS approach for efficient traversal of consecutive image frames. Our approach solves the unstable nature of depth values in static scenes that is perceived as flickering. We obtain very good result both for static and moving objects inside a scene.

Recommender systems are commonly evaluated by trying to predict known, withheld, ratings for a set of users. Measures such as the Root-Mean-Square Error are used to estimate the quality of the recommender algorithms. This process does however not acknowledge the inherent rating inconsistencies of users. In this paper we present the first results from a noise measurement user study for estimating the magic barrier of recommender systems conducted on a commercial movie recommendation community. The magic barrier is the expected squared error of the optimal recommendation algorithm, or, the lowest error we can expect from any recommendation algorithm. Our results show that the barrier can be estimated by collecting the opinions of users on already rated items.

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80% of the amount of monitored features.

Feedback on past queries is a valuable resource for improving retrieval performance on new queries. We introduce a modular approach to incorporating feedback information into given retrieval architectures. We propose to fusion the original ranking with those returned by rerankers, each of which trained on feedback given for a distinct, single query. Here, we examine the basic case of improving a query's original ranking qtest by only using one reranker: the one trained on feedback on the "closest" query qtrain. We examine the use of various distance measures between queries to first identify qtrain and then determine the best linear combination of the original and the reranker's ratings, that is: to find out which feedback to learn from, and how strongly to use it. We show the cosine distance between the term vectors of the two queries, each enriched by representations of the top N originally returned documents, to reliably answer both questions. The fusion performs equally well or better than a) always using only the original ranker or the reranker, b) selecting a hard distance threshold to decide between the two, or c) fusioning results with a ratio that is globally optimized, but fixed across all tested queries.