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In this paper, we present an approximate algorithm that is able to quickly modify a large distributed fc-nn graph by adding or removing nodes. The algorithm produces an approximate graph that is highly similar to the graph computed using a naïve approach, although it requires the computation of far fewer similarities. To achieve this goal, it relies on a novel, distributed graph based search procedure...
During the recent decade, coherent MIMO radar systems have been examined intensively. Although well proven conventional modulation schemes can be applied to coherent MIMO radars, such as linear frequency modulation (LFM), the system implementation, and therefore also the modulation, usually underlies errors such as non-linearities, phase noise etc. Nevertheless, the academic world in turn has developed...
This paper covers coherent MIMO radar systems with collocated antennas combined with minimum redundancy principles. The fundament of the first are virtual arrays which can be considered as discrete convolution of transmitter and receiver distributions. This technique can be connected to sparse arrays regarding minimum redundancy (MR) aspects. However, MR setups are usually suited for so-called uncorrelated...
In this paper we present an overview of the most important views on situation awareness in literature. We then go on to apply these concepts to cyberdefense. The main contribution of the paper lies in bringing together different decision making models and proposing a unified cyberdefense situation awareness model, that covers the different levels of abstraction from raw data to understanding, as well...
Advanced Persistent Threats (APTs) are targeted cyber attacks committed over a long period of time by highly skilled attackers. The ever increasing number of successful attacks indicates that classical network protection solutions (firewalls, Intrusion Detections Systems, proxies etc.) are no longer sufficient. Therefore, in this paper we propose a new system that combines multiples approaches using...
In this paper we present our new design of NNCTPH, a scalable algorithm to build an approximate k-NN graph from large text datasets. The algorithm uses a modified version of Context Triggered Piecewise Hashing to bin the input data into buckets, and uses NN-Descent, a versatile graph-building algorithm, inside each bucket. We use datasets consisting of the subject of spam emails to experimentally...
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