@phdthesis{SadrAzodi2015,
  author    = {Sadr-Azodi, Amir Shahab},
  title     = {Towards Real-time SIEM-based Network monitoring and Intrusion Detection through Advanced Event Normalization},
  school      = {Universit{\"a}t Potsdam},
  pages     = {144},
  year      = {2015},
  language  = {en}
}
@phdthesis{Gericke2014,
  author    = {Gericke, Lutz},
  title     = {Tele-Board - Supporting and analyzing creative collaboration in synchronous and asynchronous scenario},
  pages     = {186},
  year      = {2014},
  language  = {en}
}
@phdthesis{Makowski2021,
  author    = {Makowski, Silvia},
  title     = {Discriminative Models for Biometric Identification using Micro- and Macro-Movements of the Eyes},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 91},
  year      = {2021},
  abstract  = {Human visual perception is an active process. Eye movements either alternate between fixations and saccades or follow a smooth pursuit movement in case of moving targets. Besides these macroscopic gaze patterns, the eyes perform involuntary micro-movements during fixations which are commonly categorized into micro-saccades, drift and tremor. Eye movements are frequently studied in cognitive psychology, because they reflect a complex interplay of perception, attention and oculomotor control. A common insight of psychological research is that macro-movements are highly individual. Inspired by this finding, there has been a considerable amount of prior research on oculomotoric biometric identification. However, the accuracy of known approaches is too low and the time needed for identification is too long for any practical application. This thesis explores discriminative models for the task of biometric identification. Discriminative models optimize a quality measure of the predictions and are usually superior to generative approaches in discriminative tasks. However, using discriminative models requires to select a suitable form of data representation for sequential eye gaze data; i.e., by engineering features or constructing a sequence kernel and the performance of the classification model strongly depends on the data representation. We study two fundamentally different ways of representing eye gaze within a discriminative framework. In the first part of this thesis, we explore the integration of data and psychological background knowledge in the form of generative models to construct representations. To this end, we first develop generative statistical models of gaze behavior during reading and scene viewing that account for viewer-specific distributional properties of gaze patterns. In a second step, we develop a discriminative identification model by deriving Fisher kernel functions from these and several baseline models. We find that an SVM with Fisher kernel is able to reliably identify users based on their eye gaze during reading and scene viewing. However, since the generative models are constrained to use low-frequency macro-movements, they discard a significant amount of information contained in the raw eye tracking signal at a high cost: identification requires about one minute of input recording, which makes it inapplicable for real world biometric systems. In the second part of this thesis, we study a purely data-driven modeling approach. Here, we aim at automatically discovering the individual pattern hidden in the raw eye tracking signal. To this end, we develop a deep convolutional neural network DeepEyedentification that processes yaw and pitch gaze velocities and learns a representation end-to-end. Compared to prior work, this model increases the identification accuracy by one order of magnitude and the time to identification decreases to only seconds. The DeepEyedentificationLive model further improves upon the identification performance by processing binocular input and it also detects presentation-attacks. We find that by learning a representation, the performance of oculomotoric identification and presentation-attack detection can be driven close to practical relevance for biometric applications. Eye tracking devices with high sampling frequency and precision are expensive and the applicability of eye movement as a biometric feature heavily depends on cost of recording devices. In the last part of this thesis, we therefore study the requirements on data quality by evaluating the performance of the DeepEyedentificationLive network under reduced spatial and temporal resolution. We find that the method still attains a high identification accuracy at a temporal resolution of only 250 Hz and a precision of 0.03 degrees. Reducing both does not have an additive deteriorating effect.},
  language  = {en}
}
@phdthesis{Schapranow2012,
  author    = {Schapranow, Matthieu-Patrick},
  title     = {Real-time security extensions for EPCglobal networks},
  address   = {Potsdam},
  pages     = {XVII, 108, XXX S.},
  year      = {2012},
  language  = {en}
}
@phdthesis{Weidling2016,
  author    = {Weidling, Stefan},
  title     = {Neue Ans{\"a}tze zur Verbesserung der Fehlertoleranz gegen{\"u}ber transienten Fehlern in sequentiellen Schaltungen},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XII, 181},
  year      = {2016},
  language  = {de}
}
@phdthesis{Niess2016,
  author    = {Nieß, G{\"u}nther},
  title     = {Modellierung und Erkennung von technischen Fehlern mittels linearer und nichtlinearer Codes},
  school      = {Universit{\"a}t Potsdam},
  pages     = {V, 97},
  year      = {2016},
  language  = {de}
}
@phdthesis{Hosp2015,
  author    = {Hosp, Sven},
  title     = {Modifizierte Cross-Party Codes zur schnellen Mehrbit-Fehlerkorrektur},
  pages     = {105},
  year      = {2015},
  language  = {de}
}
@phdthesis{Duchrau2024,
  author    = {Duchrau, Georg},
  title     = {M{\"o}glichkeiten und Grenzen des erweiterten Cross Parity Codes},
  school      = {Universit{\"a}t Potsdam},
  pages     = {93},
  year      = {2024},
  language  = {de}
}
@phdthesis{Kaminski2023,
  author    = {Kaminski, Roland},
  title     = {Complex reasoning with answer set programming},
  school      = {Universit{\"a}t Potsdam},
  pages     = {301},
  year      = {2023},
  abstract  = {Answer Set Programming (ASP) allows us to address knowledge-intensive search and optimization problems in a declarative way due to its integrated modeling, grounding, and solving workflow. A problem is modeled using a rule based language and then grounded and solved. Solving results in a set of stable models that correspond to solutions of the modeled problem. In this thesis, we present the design and implementation of the clingo system---perhaps, the most widely used ASP system. It features a rich modeling language originating from the field of knowledge representation and reasoning, efficient grounding algorithms based on database evaluation techniques, and high performance solving algorithms based on Boolean satisfiability (SAT) solving technology. The contributions of this thesis lie in the design of the modeling language, the design and implementation of the grounding algorithms, and the design and implementation of an Application Programmable Interface (API) facilitating the use of ASP in real world applications and the implementation of complex forms of reasoning beyond the traditional ASP workflow.},
  language  = {en}
}
@phdthesis{Kaufmann2015,
  author    = {Kaufmann, Benjamin},
  title     = {High performance answer set solving},
  pages     = {182},
  year      = {2015},
  language  = {en}
}