@article{BlanchardFlaskaHandyetal.2016,
  author    = {Blanchard, Gilles and Flaska, Marek and Handy, Gregory and Pozzi, Sara and Scott, Clayton},
  title     = {Classification with asymmetric label noise: Consistency and maximal denoising},
  series = {Electronic journal of statistics},
  volume    = {10},
  journal   = {Electronic journal of statistics},
  publisher = {Institute of Mathematical Statistics},
  address   = {Cleveland},
  issn      = {1935-7524},
  doi       = {10.1214/16-EJS1193},
  pages     = {2780 -- 2824},
  year      = {2016},
  abstract  = {In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. In this work, we give conditions that are necessary and sufficient for the true class-conditional distributions to be identifiable. These conditions are weaker than those analyzed previously, and allow for the classes to be nonseparable and the noise levels to be asymmetric and unknown. The conditions essentially state that a majority of the observed labels are correct and that the true class-conditional distributions are "mutually irreducible," a concept we introduce that limits the similarity of the two distributions. For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions. Our results are facilitated by a connection to "mixture proportion estimation," which is the problem of estimating the maximal proportion of one distribution that is present in another. We establish a novel rate of convergence result for mixture proportion estimation, and apply this to obtain consistency of a discrimination rule based on surrogate loss minimization. Experimental results on benchmark data and a nuclear particle classification problem demonstrate the efficacy of our approach.},
  language  = {en}
}
@article{RichterDoellner2014,
  author    = {Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Concepts and techniques for integration, analysis and visualization of massive 3D point clouds},
  series = {Computers, environment and urban systems},
  volume    = {45},
  journal   = {Computers, environment and urban systems},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0198-9715},
  doi       = {10.1016/j.compenvurbsys.2013.07.004},
  pages     = {114 -- 124},
  year      = {2014},
  abstract  = {Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges.},
  language  = {en}
}
@article{WestphalAxelssonNeuhausetal.2014,
  author    = {Westphal, Florian and Axelsson, Stefan and Neuhaus, Christian and Polze, Andreas},
  title     = {VMI-PL: A monitoring language for virtual platforms using virtual machine introspection},
  series = {Digital Investigation : the international journal of digital forensics \& incident response},
  volume    = {11},
  journal   = {Digital Investigation : the international journal of digital forensics \& incident response},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {1742-2876},
  doi       = {10.1016/j.diin.2014.05.016},
  pages     = {S85 -- S94},
  year      = {2014},
  abstract  = {With the growth of virtualization and cloud computing, more and more forensic investigations rely on being able to perform live forensics on a virtual machine using virtual machine introspection (VMI). Inspecting a virtual machine through its hypervisor enables investigation without risking contamination of the evidence, crashing the computer, etc. To further access to these techniques for the investigator/researcher we have developed a new VMI monitoring language. This language is based on a review of the most commonly used VMI-techniques to date, and it enables the user to monitor the virtual machine's memory, events and data streams. A prototype implementation of our monitoring system was implemented in KVM, though implementation on any hypervisor that uses the common x86 virtualization hardware assistance support should be straightforward. Our prototype outperforms the proprietary VMWare VProbes in many cases, with a maximum performance loss of 18\% for a realistic test case, which we consider acceptable. Our implementation is freely available under a liberal software distribution license. (C) 2014 Digital Forensics Research Workshop. Published by Elsevier Ltd. All rights reserved.},
  language  = {en}
}