@phdthesis{Tiwari2019,
  author    = {Tiwari, Abhishek},
  title     = {Enhancing Users' Privacy: Static Resolution of the Dynamic Properties of Android},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 111},
  year      = {2019},
  abstract  = {The usage of mobile devices is rapidly growing with Android being the most prevalent mobile operating system. Thanks to the vast variety of mobile applications, users are preferring smartphones over desktops for day to day tasks like Internet surfing. Consequently, smartphones store a plenitude of sensitive data. This data together with the high values of smartphones make them an attractive target for device/data theft (thieves/malicious applications). Unfortunately, state-of-the-art anti-theft solutions do not work if they do not have an active network connection, e.g., if the SIM card was removed from the device. In the majority of these cases, device owners permanently lose their smartphone together with their personal data, which is even worse. Apart from that malevolent applications perform malicious activities to steal sensitive information from smartphones. Recent research considered static program analysis to detect dangerous data leaks. These analyses work well for data leaks due to inter-component communication, but suffer from shortcomings for inter-app communication with respect to precision, soundness, and scalability. This thesis focuses on enhancing users' privacy on Android against physical device loss/theft and (un)intentional data leaks. It presents three novel frameworks: (1) ThiefTrap, an anti-theft framework for Android, (2) IIFA, a modular inter-app intent information flow analysis of Android applications, and (3) PIAnalyzer, a precise approach for PendingIntent vulnerability analysis. ThiefTrap is based on a novel concept of an anti-theft honeypot account that protects the owner's data while preventing a thief from resetting the device. We implemented the proposed scheme and evaluated it through an empirical user study with 35 participants. In this study, the owner's data could be protected, recovered, and anti-theft functionality could be performed unnoticed from the thief in all cases. IIFA proposes a novel approach for Android's inter-component/inter-app communication (ICC/IAC) analysis. Our main contribution is the first fully automatic, sound, and precise ICC/IAC information flow analysis that is scalable for realistic apps due to modularity, avoiding combinatorial explosion: Our approach determines communicating apps using short summaries rather than inlining intent calls between components and apps, which requires simultaneously analyzing all apps installed on a device. We evaluate IIFA in terms of precision, recall, and demonstrate its scalability to a large corpus of real-world apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or more apps/components. PIAnalyzer proposes a novel approach to analyze PendingIntent related vulnerabilities. PendingIntents are a powerful and universal feature of Android for inter-component communication. We empirically evaluate PIAnalyzer on a set of 1000 randomly selected applications and find 1358 insecure usages of PendingIntents, including 70 severe vulnerabilities.},
  language  = {en}
}
@phdthesis{Schneider2019,
  author    = {Schneider, Jan Niklas},
  title     = {Computational approaches for emotion research},
  doi       = {10.25932/publishup-45927},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-459275},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xv, 145},
  year      = {2019},
  abstract  = {Emotionen sind ein zentrales Element menschlichen Erlebens und spielen eine wichtige Rolle bei der Entscheidungsfindung. Diese Dissertation identifiziert drei methodische Probleme der aktuellen Emotionsforschung und zeigt auf, wie diese mittels computergest{\"u}tzter Methoden gel{\"o}st werden k{\"o}nnen. Dieser Ansatz wird in drei Forschungsprojekten demonstriert, die die Entwicklung solcher Methoden sowie deren Anwendung auf konkrete Forschungsfragen beschreiben. Das erste Projekt beschreibt ein Paradigma welches es erm{\"o}glicht, die subjektive und objektive Schwierigkeit der Emotionswahrnehmung zu messen. Dar{\"u}ber hinaus erm{\"o}glicht es die Verwendung einer beliebigen Anzahl von Emotionskategorien im Vergleich zu den {\"u}blichen sechs Kategorien der Basisemotionen. Die Ergebnisse deuten auf eine Zunahme der Schwierigkeiten bei der Wahrnehmung von Emotionen mit zunehmendem Alter der Darsteller hin und liefern Hinweise darauf, dass junge Erwachsene, {\"a}ltere Menschen und M{\"a}nner ihre Schwierigkeit bei der Wahrnehmung von Emotionen untersch{\"a}tzen. Weitere Analysen zeigten eine geringe Relevanz personenbezogener Variablen und deuteten darauf hin, dass die Schwierigkeit der Emotionswahrnehmung vornehmlich durch die Auspr{\"a}gung der Wertigkeit des Ausdrucks bestimmt wird. Das zweite Projekt zeigt am Beispiel von Arousal, einem etablierten, aber vagen Konstrukt der Emotionsforschung, wie Face-Tracking-Daten dazu genutzt werden k{\"o}nnen solche Konstrukte zu sch{\"a}rfen. Es beschreibt, wie aus Face-Tracking-Daten Maße f{\"u}r die Entfernung, Geschwindigkeit und Beschleunigung von Gesichtsausdr{\"u}cken berechnet werden k{\"o}nnen. Das Projekt untersuchte wie diesen Maße mit der Arousal-Wahrnehmung in Menschen mit und ohne Autismus zusammenh{\"a}ngen. Der Abstand zum Neutralgesicht war pr{\"a}diktiv f{\"u}r die Arousal-Bewertungen in beiden Gruppen. Die Ergebnisse deuten auf eine qualitativ {\"a}hnliche Wahrnehmung von Arousal f{\"u}r Menschen mit und ohne Autismus hin. Im dritten Projekt stellen wir die Partial-Least-Squares-Analyse als allgemeine Methode vor, um eine optimale Repr{\"a}sentation zur Verkn{\"u}pfung zweier hochdimensionale Datens{\"a}tze zu finden. Das Projekt demonstriert die Anwendbarkeit dieser Methode in der Emotionsforschung anhand der Frage nach Unterschieden in der Emotionswahrnehmung zwischen M{\"a}nnern und Frauen. Wir konnten zeigen, dass die emotionale Wahrnehmung von Frauen systematisch mehr Varianz der Gesichtsausdr{\"u}cke erfasst und dass signifikante Unterschiede in der Art und Weise bestehen, wie Frauen und M{\"a}nner einige Gesichtsausdr{\"u}cke wahrnehmen. Diese konnten wir als dynamische Gesichtsausdr{\"u}cke visualisieren. Um die Anwendung der entwickelten Methode f{\"u}r die Forschungsgemeinschaft zu erleichtern, wurde ein Software-Paket f{\"u}r die Statistikumgebung R geschrieben. Zudem wurde eine Website entwickelt (thisemotiondoesnotexist.com), die es Besuchern erlaubt, ein Partial-Least-Squares-Modell von Emotionsbewertungen und Face-Tracking-Daten interaktiv zu erkunden, um die entwickelte Methode zu verbreiten und ihren Nutzen f{\"u}r die Emotionsforschung zu illustrieren.},
  language  = {en}
}
@phdthesis{Boehne2019,
  author    = {B{\"o}hne, Sebastian},
  title     = {Different degrees of formality},
  doi       = {10.25932/publishup-42379},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423795},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VI, 167},
  year      = {2019},
  abstract  = {In this thesis we introduce the concept of the degree of formality. It is directed against a dualistic point of view, which only distinguishes between formal and informal proofs. This dualistic attitude does not respect the differences between the argumentations classified as informal and it is unproductive because the individual potential of the respective argumentation styles cannot be appreciated and remains untapped. This thesis has two parts. In the first of them we analyse the concept of the degree of formality (including a discussion about the respective benefits for each degree) while in the second we demonstrate its usefulness in three case studies. In the first case study we will repair Haskell B. Curry's view of mathematics, which incidentally is of great importance in the first part of this thesis, in light of the different degrees of formality. In the second case study we delineate how awareness of the different degrees of formality can be used to help students to learn how to prove. Third, we will show how the advantages of proofs of different degrees of formality can be combined by the development of so called tactics having a medium degree of formality. Together the three case studies show that the degrees of formality provide a convincing solution to the problem of untapped potential.},
  language  = {en}
}
@phdthesis{AbdelwahabHusseinAbdelwahabElsayed2019,
  author    = {Abdelwahab Hussein Abdelwahab Elsayed, Ahmed},
  title     = {Probabilistic, deep, and metric learning for biometric identification from eye movements},
  doi       = {10.25932/publishup-46798},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-467980},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vi, 65},
  year      = {2019},
  abstract  = {A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.},
  language  = {en}
}