We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of knowledge representation formalisms and we study its properties. Syntactically, we consider adding probabilities to the formulas of a given base logic. Semantically, we define a probability distribution over the subsets of a knowledge base by taking the probabilities of the formulas into account accordingly. This gives rise to a probabilistic entailment relation that can be used for uncertain reasoning. Our approach is a generalization of many concrete probabilistic enrichments of existing approaches, such as ProbLog (an approach to probabilistic logic programming) and the constellation approach to abstract argumentation.
Analyses of graphical interface usability commonly inquire how a static stimulus (a static Web page, a static Swing interface, etc.) is aligned with responses of users, such as mouse or eye movements, mouse clicks or keyboard entries. Then, a usability expert may interpret the numerical statistics, or a visualization thereof, gain insights and suggest improvements to the graphical user interface (GUI). Nowadays, however, GUIs exhibit rich behaviors of their own, choreographing dynamic elements and orchestrating dynamic reactions to user responses rendering common methods for analysis of GUI usability increasingly difficult, if not impossible.
Real networks rarely grow uniformly in time. However, this aspect is not considered in the original Barabasi-Albert model and is mostly overlooked by its kin. The linear growth of the network results in the early mover advantage. We show empirical evidences that in real networks degree growth of nodes is, on the contrary, often time-invariant, i.e., node degree grows with time, on average, in the same way regardless of the node's time of appearance. While different growth forms of the network can be considered, we show that the exponential growth is not only approximately fulfilled in many real systems but also the key to the time-invariant degree growth of nodes.
Studying human communication and its effects increasingly requires analysis of various sources of online data and large archives of news and political texts. Therefore, the Vrije Universiteit Amsterdam and the University of Amsterdam started a collaboration last year to develop and stimulate the use of computational methods in communication science research. – the Computational Communication Science Lab Amsterdam. This presentation will give a short overview of the different projects and tools that are currently part of this project to point to possible avenues for interdisciplinary cooperation. All our tools, analyses and results are as transparent, open and easy to share and reuse as possible.
While probabilistic programming is a powerful tool, uncertainty is not always of a probabilistic kind. Some types of uncertainty are better captured using ranking theory, which is an alternative to probability theory where uncertainty is measured using degrees of surprise on the integer scale from 0 to ∞. Here we combine probabilistic programming methodology with ranking theory and develop a ranked programming language. We use the Scheme programming language a basis and extend it with the ability to express both normal and exceptional behavior of a model, and perform inference on such models.
Convolutional Neural Networks werden bereits in vielen Anwendungsbereichen erfolgreich genutzt. Vor allem im Bereich der Bildklassifizierung erreichen Convolutional-Neural-Network-Architekturen gute Resultate. In dieser Arbeit werden gängige Convlutional-Neural Network-Architekturen aus der Bildklassifizierung in einem neuen Aufgabengebiet, namentlich der Vorhersage von Gefechten im Echtzeit Strategiespiel StarCraft II im Bezug auf ihre Performance in der neuen Domäne verglichen. StarCraft II ist in diversen Bereichen des Machine Learning Anschauungsobjekt für unterschiedliche Aufgaben und dient mit seiner Python-Schnittstelle pysc2 als optimales neues Aufgabengebiet.
Text generation represents an active research field, especially with generative models. This thesis uses the Generative Adversarial Networks architecture for the purpose of names generation. This architecture was not initially designed to work on discrete data, but thanks to its high performance in other fields (e.g. computer vision) and the advantage of easy data creation, the ongoing research of the community tries to answer the question if the above mentioned architecture is suitable for text (here: name) generation.
The discrete nature of names makes it difficult to leverage the potential of this framework. The Gumbel-Softmax reparameterization technique proposed in the literature is used in this thesis to overcome this drawback.
In the EXCITE project, we have narrowed the gap of the lag between social sciences and other fields in terms of citation data availability. This has been achieved by extracting, parsing and matching references from PDF documents. During the project, we have found that a lot of references are not indexed in any bibliographic index.
Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models, such as Wordscores and Wordfish, scale texts based on relative word usage; by doing so, they do not take into consideration topical information and cannot be used for cross-lingual analyses. In our talk, we present our efforts toward developing topic-based and semantically aware text scaling approaches.
Gaze-based text entry systems have been an important means of communication for people with motor disabilities. Although several dwell-time and dwell-free tools have been developed to facilitate the process of gaze-based text entry, still the typing speed is quite slow and the cognitive load is rather high. Moreover, most previous methods are developed only based on gaze and fixations sequence. However, these methods can result in lengthy amounts of time for typing. Besides, users cannot always perfectly gaze at every key in many cases. In this thesis, we propose TGSBoard an onscreen keyboard that combines the simplicity and accuracy of touch inputs with the speed of eye typing by gaze swiping to provide efficient and comfortable dwell-free text entry.