The master’s thesis deals with computer networks, which are currently the global communication infrastructure and play a very important role in today’s society. Most of us can be unaware of how often we interact with these networks. We almost constantly come into contact with them. They allow us to communicate through the Internet via services such as: email, skype, icq, facebook, etc... The work focuses on the design and implementation of a robot designed for exploration and mapping of computer network topology and protocols HTTP and ICMP. The theoretical part is dedicated to computer networks, seventh and third-layer ISO/OSI model, protocol HTTP and ICMP attacks against DNS, DoS attacks and detection systems and avoiding distortaions.

Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be powerful diffusion mechanisms of both misinformation and propaganda. In some cases these groups users advocate actions or policies that could be construed as extreme along nearly any distribution of opinion, and are thus called Online Extremist Communities (OECs). Although these communities appear increasingly common, little is known about how these groups form or the methods used to influence them. The work in this thesis provides researchers a methodological framework to study these groups by answering three critical research questions: How can we detect large dynamic online activist or extremist communities? What automated tools are used to build, isolate, and influence these communities? What methods can be used to gain novel insight into large online activist or extremist communities? These group members social ties can be inferred based on the various affordances offered by OSNs for group curation. By developing heterogeneous, annotated graph representations of user behavior I can efficiently extract online activist discussion cores using an ensemble of unsupervised machine learning methods. I call this technique Ensemble Agreement Clustering. Through manual inspection, these discussion cores can then often be used as training data to detect the larger community. I present a novel supervised learning algorithm called Multiplex Vertex Classification for network bipartition on heterogeneous, annotated graphs. This methodological pipeline has also proven useful for social botnet detection, and a study of large, complex social botnets used for propaganda dissemination is provided as well. Throughout this thesis I provide Twitter case studies including communities focused on the Islamic State of Iraq and al-Sham (ISIS), the ongoing Syrian Revolution, the Euromaidan Movement in Ukraine, as well as the alt-Right.

This thesis deals with the detection of intracranial haemorrhages and their type classification in head CT images. The method of haemorrhages detection is based on a series of classifiers of the presence and type of haemorrhages in 2D CT slices in axial, sagittal and coronal plane, that may localise the bleedings and determine their types. The classifiers are based on the convolutional neural network architecture Inception-ResNet-v2. The head CT dataset CQ500 which is made available for public access, is used for the experiments. The thesis describes an additional manual annotation of the data, as the available annotations are insufficient for the purposes of the experiments. This thesis includes a theoretical basis of the essential medical knowledge, machine learning based classification and detection methods, and the detection algorithm proposal, realisation and testing. The algorithm performance is evaluated and discussed together with the potential implementation of the algorithm in computer-aided diagnosis systems.

Dünne Silizium- und Germaniumdioxidfilme auf Ru(0001)-Kristallen werden hinsichtlich dynamischer Prozesse untersucht. Zwischen Oxidfilm und Substrat befinden sich Sauerstoffatome, die eine ent-scheidende Rolle in diesen Systemen spielen. Zunächst werden diese Sauerstofflagen auf Ru(0001) mittels Hochgeschwindigkeits-Rastertunnelmikroskopie (STM) analysiert. Daraufhin wird die GeO2-Monolage auf Ru(0001) bei hohen Bildraten mit einer selbstentwickelten halbautomatischen Netz-werkdetektion untersucht. Schließlich wird die SiO2-Bilage auf Ru(0001) mit konventionellen sowie mit schnellen STM-Messungen bei Raumtemperatur und bei 600 K abgebildet. Um schnelle Messungen bei hohen Temperaturen zu realisieren, wird ein Hochgeschwindigkeits-STM konstruiert, welches bei unterschiedlichen Temperaturen betrieben werden kann. Unkon-ventionelle Spiralgeometrien ermöglichen verzerrungsfreie Bilder in weniger als 10 ms aufzunehmen. Die adsorbierten Sauerstofflagen werden erstmals bei hohen Bildraten untersucht. Die experimen-tellen Ergebnisse werden durch extern durchgeführte Dichtefunktionaltheorie-Berechnungen ergänzt. In den auf Ru(0001) bei Raumtemperatur stabilen Sauerstofflagen O(2×2), O(2×1) und 3O(2×2) werden dynamische Prozesse beobachtet. Die Besetzung des Zwischenzustandes entlang des Diffusionspfades und schnelle "Umklapp"-Prozesse eindimensionaler Linien werden auf atomarer Ebene aufgelöst. Komplexe Domänengrenzen in der GeO2-Monolage auf Ru(0001) werden mit Hochgeschwindigkeits-STM abgebildet. Die Messungen an der SiO2-Bilage auf Ru(0001) zeigen dynamische Änderungen des Abbildungskontrasts, die mit den mobilen Sauertsoffatomen an der Grenzfläche zusammenhängen können. Messungen bei hohen Temperaturen zeigen dynamische Kontraständerungen von mesoskopischen Strukturen. Diese Messungen stellen die ersten schnellen Hochtemperatur-STM-Aufnahmen des Siliziumdioxidfilms dar und bilden die Grundlage für künftige Studien zu dynamischen Veränderungen in dünnen Oxidschichtsystemen. / Dynamics related to thin silicon- and germanium dioxide films that are grown on Ru(0001) crystals are investigated. Between the film and the metal support oxygen species are present that play a crucial role for these film systems. First, these oxygen adlayers on Ru(0001) are analyzed by high-speed scan-ning tunneling microscopy (STM) with the focus on dynamic processes. In a next step, the monolayer of germanium dioxide (germania) supported on Ru(0001) is studied at elevated frame rates and with a self-designed semi-automated network detection. Finally, the bilayer of silicon dioxide (silica) on Ru(0001) is studied by conventional and by high-speed STM both at room temperature and at 600 K. To realize fast STM measurements at elevated temperatures, a high-speed STM is designed that can operate at variable temperatures. Images are acquired in less than 10 ms with unconventional spiral scan patterns. The dynamics in oxygen adlayers are investigated for the first time at elevated frame rates. Experimental results are supported by density functional theory (DFT) calculations performed externally. Dynamic events are observed in the oxygen adlayers that are stable on Ru(0001) at room temperature, namely O(2×2), O(2×1), and 3O(2×2). The occupation of an intermediate state along the oxygen diffusion pathway and fast "flipping" events of atomic one-dimensional stripe patterns are observed. On the germania monolayer on Ru(0001), complex domain boundary structures are resolved with high-speed STM. In high-speed scans on the silica bilayer on Ru(0001), dynamic changes of the imaging contrast are observed that may relate to the mobile species in the oxygen interfacial layer. Measurements at elevated temperature reveal dynamic contrast changes of mesoscopic features. These measurements constitute the first high-speed STM scans on the silica film at elevated temperatures and form the basis for future studies with the focus on dynamic processes in thin oxide film systems.