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Institute
- Institut für Informatik und Computational Science (1111) (remove)
Das größte der fächerübergreifenden Projekte im Potsdamer Projekt Qualitätspakt Lehre hatte die flächendeckende Etablierung von digitalen Medien als einen integralen Bestandteil von Lehre und Studium zum Gegenstand. Im Teilprojekt E-Learning in Studienbereichen (eLiS) wurden dafür Maßnahmen in den Feldern Organisations-, technische und Inhaltsentwicklung zusammengeführt. Der vorliegende Beitrag präsentiert auf Basis von Ausgangslage und Zielsetzungen die Ergebnisse rund um die Digitalisierung von Lehre und Studium an der Universität Potsdam. Exemplarisch werden fünf Dienste näher vorgestellt, die inzwischen größtenteils in den Regelbetrieb der Hochschule übergegangen sind: die Videoplattform Media.UP, die mobile App Reflect.UP, die persönliche Lernumgebung Campus. UP, das Self-Service-Portal Cook.UP und das Anzeigesystem Freiraum.UP. Dabei wird jeweils ein technischer Blick „unter die Haube“ verbunden mit einer Erläuterung der Nutzungsmöglichkeiten, denen eine aktuelle Einschätzung von Lehrenden und Studierenden der Hochschule gegenübergestellt wird. Der Beitrag schließt mit einer Einbettung der vorgestellten Entwicklungen in einen größeren Kontext und einem Ausblick auf die weiterhin anstehenden Aufgaben.
Full error detection and correction method applied on pipelined structure using two approaches
(2020)
In this paper, two approaches are evaluated using the Full Error Detection and Correction (FEDC) method for a pipelined structure. The approaches are referred to as Full Duplication with Comparison (FDC) and Concurrent Checking with Parity Prediction (CCPP). Aforementioned approaches are focused on the borderline cases of FEDC method which implement Error Detection Circuit (EDC) in two manners for the purpose of protection of combinational logic to address the soft errors of unspecified duration. The FDC approach implements a full duplication of the combinational circuit, as the most complex and expensive implementation of the FEDC method, and the CCPP approach implements only the parity prediction bit, being the simplest and cheapest technique, for soft error detection. Both approaches are capable of detecting soft errors in the combinational logic, with single faults being injected into the design. On the one hand, the FDC approach managed to detect and correct all injected faults while the CCPP approach could not detect multiple faults created at the output of combinational circuit. On the other hand, the FDC approach leads to higher power consumption and area increase compared to the CCPP approach.
A triple modular redundancy (TMR) based design technique for double cell upsets (DCUs) mitigation is investigated in this paper. This technique adds three extra self-voter circuits into a traditional TMR structure to enable the enhanced error correction capability. Fault-injection simulations show that the soft error rate (SER) of the proposed technique is lower than 3% of that of TMR. The implementation of this proposed technique is compatible with the automatic digital design flow, and its applicability and performance are evaluated on an FIFO circuit.
Die Fehlerkorrektur in der Codierungstheorie beschäftigt sich mit der Erkennung und Behebung von Fehlern bei der Übertragung und auch Sicherung von Nachrichten.
Hierbei wird die Nachricht durch zusätzliche Informationen in ein Codewort kodiert.
Diese Kodierungsverfahren besitzen verschiedene Ansprüche, wie zum Beispiel die maximale Anzahl der zu korrigierenden Fehler und die Geschwindigkeit der Korrektur.
Ein gängiges Codierungsverfahren ist der BCH-Code, welches industriell für bis zu vier Fehler korrigiere Codes Verwendung findet. Ein Nachteil dieser Codes ist die technische Durchlaufzeit für die Berechnung der Fehlerstellen mit zunehmender Codelänge.
Die Dissertation stellt ein neues Codierungsverfahren vor, bei dem durch spezielle Anordnung kleinere Codelängen eines BCH-Codes ein langer Code erzeugt wird. Diese Anordnung geschieht über einen weiteren speziellen Code, einem LDPC-Code, welcher für eine schneller Fehlererkennung konzipiert ist.
Hierfür wird ein neues Konstruktionsverfahren vorgestellt, welches einen Code für einen beliebige Länge mit vorgebbaren beliebigen Anzahl der zu korrigierenden Fehler vorgibt. Das vorgestellte Konstruktionsverfahren erzeugt zusätzlich zum schnellen Verfahren der Fehlererkennung auch eine leicht und schnelle Ableitung eines Verfahrens zu Kodierung der Nachricht zum Codewort. Dies ist in der Literatur für die LDPC-Codes bis zum jetzigen Zeitpunkt einmalig.
Durch die Konstruktion eines LDPC-Codes wird ein Verfahren vorgestellt wie dies mit einem BCH-Code kombiniert wird, wodurch eine Anordnung des BCH-Codes in Blöcken erzeugt wird. Neben der allgemeinen Beschreibung dieses Codes, wird ein konkreter Code für eine 2-Bitfehlerkorrektur beschrieben. Diese besteht aus zwei Teilen, welche in verschiedene Varianten beschrieben und verglichen werden. Für bestimmte Längen des BCH-Codes wird ein Problem bei der Korrektur aufgezeigt, welche einer algebraischen Regel folgt.
Der BCH-Code wird sehr allgemein beschrieben, doch existiert durch bestimmte Voraussetzungen ein BCH-Code im engerem Sinne, welcher den Standard vorgibt. Dieser BCH-Code im engerem Sinne wird in dieser Dissertation modifiziert, so dass das algebraische Problem bei der 2-Bitfehler Korrektur bei der Kombination mit dem LDPC-Code nicht mehr existiert. Es wird gezeigt, dass nach der Modifikation der neue Code weiterhin ein BCH-Code im allgemeinen Sinne ist, welcher 2-Bitfehler korrigieren und 3-Bitfehler erkennen kann. Bei der technischen Umsetzung der Fehlerkorrektur wird des Weiteren gezeigt, dass die Durchlaufzeiten des modifizierten Codes im Vergleich zum BCH-Code schneller ist und weiteres Potential für Verbesserungen besitzt.
Im letzten Kapitel wird gezeigt, dass sich dieser modifizierte Code mit beliebiger Länge eignet für die Kombination mit dem LDPC-Code, wodurch dieses Verfahren nicht nur umfänglicher in der Länge zu nutzen ist, sondern auch durch die schnellere Dekodierung auch weitere Vorteile gegenüber einem BCH-Code im engerem Sinne besitzt.
In this paper, an asynchronous design for soft error detection and correction in combinational and sequential circuits is presented. The proposed architecture is called Asynchronous Full Error Detection and Correction (AFEDC). A custom design flow with integrated commercial EDA tools generates the AFEDC using the asynchronous bundled-data design style. The AFEDC relies on an Error Detection Circuit (EDC) for protecting the combinational logic and fault-tolerant latches for protecting the sequential logic. The EDC can be implemented using different detection methods. For this work, two boundary variants are considered, the Full Duplication with Comparison (FDC) and the Partial Duplication with Parity Prediction (PDPP). The AFEDC architecture can handle single events and timing faults of arbitrarily long duration as well as the synchronous FEDC, but additionally can address known metastability issues of the FEDC and other similar synchronous architectures and provide a more practical solution for handling the error recovery process. Two case studies are developed, a carry look-ahead adder and a pipelined non-restoring array divider. Results show that the AFEDC provides equivalent fault coverage when compared to the FEDC while reducing area, ranging from 9.6% to 17.6%, and increasing energy efficiency, which can be up to 6.5%.
We elaborate upon the theoretical foundations of a metric temporal extension of Answer Set Programming. In analogy to previous extensions of ASP with constructs from Linear Temporal and Dynamic Logic, we accomplish this in the setting of the logic of Here-and-There and its non-monotonic extension, called Equilibrium Logic. More precisely, we develop our logic on the same semantic underpinnings as its predecessors and thus use a simple time domain of bounded time steps. This allows us to compare all variants in a uniform framework and ultimately combine them in a common implementation.
Eclingo
(2020)
We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set Programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo 's efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results.
This paper continues the line of research aimed at investigating the relationship between logic programs and first-order theories. We extend the definition of program completion to programs with input and output in a subset of the input language of the ASP grounder gringo, study the relationship between stable models and completion in this context, and describe preliminary experiments with the use of two software tools, anthem and vampire, for verifying the correctness of programs with input and output. Proofs of theorems are based on a lemma that relates the semantics of programs studied in this paper to stable models of first-order formulas.
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.
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.
PLATON
(2019)
Lesson planning is both an important and demanding task—especially as part of teacher training. This paper presents the requirements for a lesson planning system and evaluates existing systems regarding these requirements. One major drawback of existing software tools is that most are limited to a text- or form-based representation of the lesson designs. In this article, a new approach with a graphical, time-based representation with (automatic) analyses methods is proposed and the system architecture and domain model are described in detail. The approach is implemented in an interactive, web-based prototype called PLATON, which additionally supports the management of lessons in units as well as the modelling of teacher and student-generated resources. The prototype was evaluated in a study with 61 prospective teachers (bachelor’s and master’s preservice teachers as well as teacher trainees in post-university teacher training) in Berlin, Germany, with a focus on usability. The results show that this approach proofed usable for lesson planning and offers positive effects for the perception of time and self-reflection.
The Surface Water and Ocean Topography (SWOT) mission is a next generation satellite mission expected to provide a 2 km-resolution observation of the sea surface height (SSH) on a two-dimensional swath. Processing SWOT data will be challenging because of the large amount of data, the mismatch between a high spatial resolution and a low temporal resolution, and the observation errors. The present paper focuses on the reduction of the spatially structured errors of SWOT SSH data. It investigates a new error reduction method and assesses its performance in an observing system simulation experiment. The proposed error-reduction method first projects the SWOT SSH onto a subspace spanned by the SWOT spatially structured errors. This projection is removed from the SWOT SSH to obtain a detrended SSH. The detrended SSH is then processed within an ensemble data assimilation analysis to retrieve a full SSH field. In the latter step, the detrending is applied to both the SWOT data and an ensemble of model-simulated SSH fields. Numerical experiments are performed with synthetic SWOT observations and an ensemble from a North Atlantic, 1/60 degrees simulation of the ocean circulation (NATL60). The data assimilation analysis is carried out with an ensemble Kalman filter. The results are assessed with root mean square errors, power spectrum density, and spatial coherence. They show that a significant part of the large scale SWOT errors is reduced. The filter analysis also reduces the small scale errors and allows for an accurate recovery of the energy of the signal down to 25 km scales. In addition, using the SWOT nadir data to adjust the SSH detrending further reduces the errors.
The target article discusses the question of how educational makerspaces can become places supportive of knowledge construction. This question is too often neglected by people who run makerspaces, as they mostly explain how to use different tools and focus on the creation of a product. In makerspaces, often pupils also engage in physical computing activities and thus in the creation of interactive artifacts containing embedded systems, such as smart shoes or wristbands, plant monitoring systems or drink mixing machines. This offers the opportunity to reflect on teaching physical computing in computer science education, where similarly often the creation of the product is so strongly focused upon that the reflection of the learning process is pushed into the background.
Multi-sided platforms (MSP) strongly affect markets and play a crucial part within the digital and networked economy. Although empirical evidence indicates their occurrence in many industries, research has not investigated the game-changing impact of MSP on traditional markets to a sufficient extent. More specifically, we have little knowledge of how MSP affect value creation and customer interaction in entire markets, exploiting the potential of digital technologies to offer new value propositions. Our paper addresses this research gap and provides an initial systematic approach to analyze the impact of MSP on the insurance industry. For this purpose, we analyze the state of the art in research and practice in order to develop a reference model of the value network for the insurance industry. On this basis, we conduct a case-study analysis to discover and analyze roles which are occupied or even newly created by MSP. As a final step, we categorize MSP with regard to their relation to traditional insurance companies, resulting in a classification scheme with four MSP standard types: Competition, Coordination, Cooperation, Collaboration.
In recent years, named entity linking (NEL) tools were primarily developed in terms of a general approach, whereas today numerous tools are focusing on specific domains such as e.g. the mapping of persons and organizations only, or the annotation of locations or events in microposts. However, the available benchmark datasets necessary for the evaluation of NEL tools do not reflect this focalizing trend. We have analyzed the evaluation process applied in the NEL benchmarking framework GERBIL [in: Proceedings of the 24th International Conference on World Wide Web (WWW’15), International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 2015, pp. 1133–1143, Semantic Web 9(5) (2018), 605–625] and all its benchmark datasets. Based on these insights we have extended the GERBIL framework to enable a more fine grained evaluation and in depth analysis of the available benchmark datasets with respect to different emphases. This paper presents the implementation of an adaptive filter for arbitrary entities and customized benchmark creation as well as the automated determination of typical NEL benchmark dataset properties, such as the extent of content-related ambiguity and diversity. These properties are integrated on different levels, which also enables to tailor customized new datasets out of the existing ones by remixing documents based on desired emphases. Besides a new system library to enrich provided NIF [in: International Semantic Web Conference (ISWC’13), Lecture Notes in Computer Science, Vol. 8219, Springer, Berlin, Heidelberg, 2013, pp. 98–113] datasets with statistical information, best practices for dataset remixing are presented, and an in depth analysis of the performance of entity linking systems on special focus datasets is presented.
In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.
Emotions are a central element of human experience. They occur with high frequency in everyday life and play an important role in decision making. However, currently there is no consensus among researchers on what constitutes an emotion and on how emotions should be investigated. This dissertation identifies three problems of current emotion research: the problem of ground truth, the problem of incomplete constructs and the problem of optimal representation. I argue for a focus on the detailed measurement of emotion manifestations with computer-aided methods to solve these problems. This approach is demonstrated in three research projects, which describe the development of methods specific to these problems as well as their application to concrete research questions.
The problem of ground truth describes the practice to presuppose a certain structure of emotions as the a priori ground truth. This determines the range of emotion descriptions and sets a standard for the correct assignment of these descriptions. The first project illustrates how this problem can be circumvented with a multidimensional emotion perception paradigm which stands in contrast to the emotion recognition paradigm typically employed in emotion research. This paradigm allows to calculate an objective difficulty measure and to collect subjective difficulty ratings for the perception of emotional stimuli. Moreover, it enables the use of an arbitrary number of emotion stimuli categories as compared to the commonly used six basic emotion categories. Accordingly, we collected data from 441 participants using dynamic facial expression stimuli from 40 emotion categories. Our findings suggest an increase in emotion perception difficulty with increasing actor age and provide evidence to suggest that young adults, the elderly and men underestimate their emotion perception difficulty. While these effects were predicted from the literature, we also found unexpected and novel results. In particular, the increased difficulty on the objective difficulty measure for female actors and observers stood in contrast to reported findings. Exploratory analyses revealed low relevance of person-specific variables for the prediction of emotion perception difficulty, but highlighted the importance of a general pleasure dimension for the ease of emotion perception.
The second project targets the problem of incomplete constructs which relates to vaguely defined psychological constructs on emotion with insufficient ties to tangible manifestations. The project exemplifies how a modern data collection method such as face tracking data can be used to sharpen these constructs on the example of arousal, a long-standing but fuzzy construct in emotion research. It describes how measures of distance, speed and magnitude of acceleration can be computed from face tracking data and investigates their intercorrelations. We find moderate to strong correlations among all measures of static information on one hand and all measures of dynamic information on the other. The project then investigates how self-rated arousal is tied to these measures in 401 neurotypical individuals and 19 individuals with autism. Distance to the neutral face was predictive of arousal ratings in both groups. Lower mean arousal ratings were found for the autistic group, but no difference in correlation of the measures and arousal ratings could be found between groups. Results were replicated in a high autistic traits group consisting of 41 participants. The findings suggest a qualitatively similar perception of arousal for individuals with and without autism. No correlations between valence ratings and any of the measures could be found which emphasizes the specificity of our tested measures for the construct of arousal.
The problem of optimal representation refers to the search for the best representation of emotions and the assumption that there is a one-fits-all solution. In the third project we introduce partial least squares analysis as a general method to find an optimal representation to relate two high-dimensional data sets to each other. The project demonstrates its applicability to emotion research on the question of emotion perception differences between men and women. The method was used with emotion rating data from 441 participants and face tracking data computed on 306 videos. We found quantitative as well as qualitative differences in the perception of emotional facial expressions between these groups. We showed that women’s emotional perception systematically captured more of the variance in facial expressions. Additionally, we could show that significant differences exist in the way that women and men perceive some facial expressions which could be visualized as concrete facial expression sequences. These expressions suggest differing perceptions of masked and ambiguous facial expressions between the sexes. In order to facilitate use of the developed method by the research community, a package for the statistical environment R was written. Furthermore, to call attention to the method and its usefulness for emotion research, a website was designed that allows users to explore a model of emotion ratings and facial expression data in an interactive fashion.
PLATON
(2019)
Lesson planning is both an important and demanding task—especially as part of teacher training. This paper presents the requirements for a lesson planning system and evaluates existing systems regarding these requirements. One major drawback of existing software tools is that most are limited to a text- or form-based representation of the lesson designs. In this article, a new approach with a graphical, time-based representation with (automatic) analyses methods is proposed and the system architecture and domain model are described in detail. The approach is implemented in an interactive, web-based prototype called PLATON, which additionally supports the management of lessons in units as well as the modelling of teacher and student-generated resources. The prototype was evaluated in a study with 61 prospective teachers (bachelor’s and master’s preservice teachers as well as teacher trainees in post-university teacher training) in Berlin, Germany, with a focus on usability. The results show that this approach proofed usable for lesson planning and offers positive effects for the perception of time and self-reflection.
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.
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.