No abstract

Denial of service (DoS) attacks are arguably one of the most cumbersome problems in the Internet. This paper presents a distributed information system (over a set of completely connected servers) called Chameleon which is robust to DoS attacks on the nodes as well as the operations of the system. In particular, it allows nodes to efficiently look up and insert data items at any time, despite a powerful "past-insider adversary" which has complete knowledge of the system up to some time point t0 and can use that knowledge in order to block a constant fraction of the nodes and inject lookup and insert requests to selected data. This is achieved with a smart randomized replication policy requiring a polylogarithmic overhead only and the interplay of a permanent and a temporary distributed hash table. All requests in Chameleon can be processed in polylogarithmic time and work at every node.

No abstract

Given a weighted bispanning graph B = (V, P, Q) consisting of two edge-disjoint spanning trees P and Q such that w(P ) < w(Q) and Q is the only spanning tree with weight w(Q), it is conjectured that there are |V | − 1 spanning trees with pairwise different weight where each of them is smaller than w(Q). This conjecture due to Mayr and Plaxton is proven for bispanning graphs restricted in terms of the underlying weight function and the structure of the bispanning graphs. Furthermore, a slightly stronger conjecture is presented and proven for the latter class.

A major focus in computer vision research is the recognition of human activity based on visual information from audiovisual data using artificial intelligence. In this context, researchers are currently exploring image-based approaches using 3D CNNs, RNNs, or hybrid models with the intent of learning multiple levels of representation and abstraction that enable fully automated feature extraction and activity analysis based on them. Unfortunately, these architectures require powerful hardware to achieve the most real-time processing possible, making them difficult to deploy on smartphones. However, many video recordings are increasingly made with smartphones, so immediate classification of performed human activities and their tagging already during video recording would be useful for a variety of use cases. Especially in the mobile environment, a wide variety of use cases are therefore conceivable, such as the detection of correct motion sequences in the sports and health sector or the monitoring and automated alerting of security-relevant environments (e.g., demonstrations, festivals). However, this requires an efficient system architecture to perform real-time analysis despite limited hardware power. This paper addresses the approach of skeleton-based activity recognition on smartphones, where motion vectors of detected skeleton points are analyzed for their spatial and temporal expression rather than pixel-based information. In this process, the 3D-bone points of a recognized person are extracted using the AR framework integrated in the operating system and their motion data is analyzed in real time using a self-trained RNN. This purely numerical approach enables time-efficient real-time processing and activity classification. This system makes it possible to recognize a person in a live video stream recorded with a smartphone and classify the activity performed. By successfully deploying the system in several field tests, it can be shown both that the described approach works in principle and that it can be transferred to a resource-constrained mobile environment.