Edge detection in unorganized 3D point cloud

The application of 3D laser scanning in the mining industry is increasing progressively over the years. This presents an opportunity to visualize and analyze the underground world and potentially save countless man- hours and exposure to safety incidents. This thesis envisions to detect the “Edges of the Rocks” in the 3D point cloud collected via scanner, although edge detection in point cloud is considered as a difficult but meaningful problem. As a solution to noisy and unorganized 3D point cloud, a new method, EdgeScan method, has been proposed and implemented to detect fast and accurate edges from the 3D point cloud for real time systems. EdgeScan method is aimed to make use of 2D edge processing techniques to represent the edge characteristics in 3D point cloud with better accuracy. A comparisons of EdgeScan method with other common edge detection methods for 3D point cloud is administered, eventually, results suggest that the stated EdgeScan method furnishes a better speed and accuracy especially for large dataset in real time systems.Master of Science (MSc) in Computational Science

Solving PDEs on Unknown Manifolds with Machine Learning

This paper proposes a mesh-free computational framework and machine learning theory for solving elliptic PDEs on unknown manifolds, identified with point clouds, based on diffusion maps (DM) and deep learning. The PDE solver is formulated as a supervised learning task to solve a least-squares regression problem that imposes an algebraic equation approximating a PDE (and boundary conditions if applicable). This algebraic equation involves a graph-Laplacian type matrix obtained via DM asymptotic expansion, which is a consistent estimator of second-order elliptic differential operators. The resulting numerical method is to solve a highly non-convex empirical risk minimization problem subjected to a solution from a hypothesis space of neural-network type functions. In a well-posed elliptic PDE setting, when the hypothesis space consists of feedforward neural networks with either infinite width or depth, we show that the global minimizer of the empirical loss function is a consistent solution in the limit of large training data. When the hypothesis space is a two-layer neural network, we show that for a sufficiently large width, the gradient descent method can identify a global minimizer of the empirical loss function. Supporting numerical examples demonstrate the convergence of the solutions and the effectiveness of the proposed solver in avoiding numerical issues that hampers the traditional approach when a large data set becomes available, e.g., large matrix inversion

Detection and Explanation of Distributed Denial of Service (DDoS) Attack Through Interpretable Machine Learning

Distributed denial of service (DDoS) is a network-based attack where the aim of the attacker is to overwhelm the victim server. The attacker floods the server by sending enormous amount of network packets in a distributed manner beyond the servers capacity and thus causing the disruption of its normal service. In this dissertation, we focus to build intelligent detectors that can learn by themselves with less human interactions and detect DDoS attacks accurately. Machine learning (ML) has promising outcomes throughout the technologies including cybersecurity and provides us with intelligence when applied on Intrusion Detection Systems (IDSs). In addition, from the state-of-the-art ML-based IDSs, the Ensemble classifier (combination of classifiers) outperforms single classifier. Therefore, we have implemented both supervised and unsupervised ensemble frameworks to build IDSs for better DDoS detection accuracy with lower false alarms compared to the existing ones. Our experimentation, done with the most popular and benchmark datasets such as NSL-KDD, UNSW-NB15, and CICIDS2017, have achieved at most detection accuracy of 99.1% with the lowest false positive rate of 0.01%. As feature selection is one of the mandatory preprocessing phases in ML classification, we have designed several feature selection techniques for better performances in terms of DDoS detection accuracy, false positive alarms, and training times. Initially, we have implemented an ensemble framework for feature selection (FS) methods which combines almost all well-known FS methods and yields better outcomes compared to any single FS method.The goal of my dissertation is not only to detect DDoS attacks precisely but also to demonstrate explanations for these detections. Interpretable machine learning (IML) technique is used to explain a detected DDoS attack with the help of the effectiveness of the corresponding features. We also have implemented a novel feature selection approach based on IML which helps to find optimum features that are used further to retrain our models. The retrained model gives better performances than general feature selection process. Moreover, we have developed an explainer model using IML that identifies detected DDoS attacks with proper explanations based on effectiveness of the features. The contribution of this dissertation is five-folded with the ultimate goal of detecting the most frequent DDoS attacks in cyber security. In order to detect DDoS attacks, we first used ensemble machine learning classification with both supervised and unsupervised classifiers. For better performance, we then implemented and applied two feature selection approaches, such as ensemble feature selection framework and IML based feature selection approach, both individually and in a combination with supervised ensemble framework. Furthermore, we exclusively added explanations for the detected DDoS attacks with the help of explainer models that are built using LIME and SHAP IML methods. To build trustworthy explainer models, a detailed survey has been conducted on interpretable machine learning methods and on their associated tools. We applied the designed framework in various domains, like smart grid and NLP-based IDS to verify its efficacy and ability of performing as a generic model

Self-organizing Network Optimization via Placement of Additional Nodes

Das Hauptforschungsgebiet des Graduiertenkollegs "International Graduate School on Mobile Communication" (GS Mobicom) der Technischen Universität Ilmenau ist die Kommunikation in Katastrophenszenarien. Wegen eines Desasters oder einer Katastrophe können die terrestrischen Elementen der Infrastruktur eines Kommunikationsnetzwerks beschädigt oder komplett zerstört werden. Dennoch spielen verfügbare Kommunikationsnetze eine sehr wichtige Rolle während der Rettungsmaßnahmen, besonders für die Koordinierung der Rettungstruppen und für die Kommunikation zwischen ihren Mitgliedern. Ein solcher Service kann durch ein mobiles Ad-Hoc-Netzwerk (MANET) zur Verfügung gestellt werden. Ein typisches Problem der MANETs ist Netzwerkpartitionierung, welche zur Isolation von verschiedenen Knotengruppen führt. Eine mögliche Lösung dieses Problems ist die Positionierung von zusätzlichen Knoten, welche die Verbindung zwischen den isolierten Partitionen wiederherstellen können. Hauptziele dieser Arbeit sind die Recherche und die Entwicklung von Algorithmen und Methoden zur Positionierung der zusätzlichen Knoten. Der Fokus der Recherche liegt auf Untersuchung der verteilten Algorithmen zur Bestimmung der Positionen für die zusätzlichen Knoten. Die verteilten Algorithmen benutzen nur die Information, welche in einer lokalen Umgebung eines Knotens verfügbar ist, und dadurch entsteht ein selbstorganisierendes System. Jedoch wird das gesamte Netzwerk hier vor allem innerhalb eines ganz speziellen Szenarios - Katastrophenszenario - betrachtet. In einer solchen Situation kann die Information über die Topologie des zu reparierenden Netzwerks im Voraus erfasst werden und soll, natürlich, für die Wiederherstellung mitbenutzt werden. Dank der eventuell verfügbaren zusätzlichen Information können die Positionen für die zusätzlichen Knoten genauer ermittelt werden. Die Arbeit umfasst eine Beschreibung, Implementierungsdetails und eine Evaluierung eines selbstorganisierendes Systems, welche die Netzwerkwiederherstellung in beiden Szenarien ermöglicht.The main research area of the International Graduate School on Mobile Communication (GS Mobicom) at Ilmenau University of Technology is communication in disaster scenarios. Due to a disaster or an accident, the network infrastructure can be damaged or even completely destroyed. However, available communication networks play a vital role during the rescue activities especially for the coordination of the rescue teams and for the communication between their members. Such a communication service can be provided by a Mobile Ad-Hoc Network (MANET). One of the typical problems of a MANET is network partitioning, when separate groups of nodes become isolated from each other. One possible solution for this problem is the placement of additional nodes in order to reconstruct the communication links between isolated network partitions. The primary goal of this work is the research and development of algorithms and methods for the placement of additional nodes. The focus of this research lies on the investigation of distributed algorithms for the placement of additional nodes, which use only the information from the nodes’ local environment and thus form a self-organizing system. However, during the usage specifics of the system in a disaster scenario, global information about the topology of the network to be recovered can be known or collected in advance. In this case, it is of course reasonable to use this information in order to calculate the placement positions more precisely. The work provides the description, the implementation details and the evaluation of a self-organizing system which is able to recover from network partitioning in both situations

Schnelle Löser für partielle Differentialgleichungen

The workshop Schnelle Löser für partielle Differentialgleichungen, organised by Randolph E. Bank (La Jolla), Wolfgang Hackbusch(Leipzig), Gabriel Wittum (Heidelberg) was held May 22nd - May 28th, 2005. This meeting was well attended by 47 participants with broad geographic representation from 9 countries and 3 continents. This workshop was a nice blend of researchers with various backgrounds

Statistics meets Machine Learning

Theory and application go hand in hand in most areas of statistics. In a world flooded with huge amounts of data waiting to be analyzed, classified and transformed into useful outputs, the designing of fast, robust and stable algorithms has never been as important as it is today. On the other hand, irrespective of whether the focus is put on estimation, prediction, classification or other purposes, it is equally crucial to provide clear guarantees that such algorithms have strong theoretical guarantees. Many statisticians, independently of their original research interests, have become increasingly aware of the importance of the numerical needs faced in numerous applications including gene expression profiling, health care, pattern and speech recognition, data security, marketing personalization, natural language processing, to name just a few. The goal of this workshop is twofold: (a) exchange knowledge on successful algorithmic approaches and discuss some of the existing challenges, and (b) to bring together researchers in statistics and machine learning with the aim of sharing expertise and exploiting possible differences in points of views to obtain a better understanding of some of the common important problems

Variational methods and its applications to computer vision

Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations. The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces