Statistical relational learning of semantic models and grammar rules for 3D building reconstruction from 3D point clouds

Formal grammars are well suited for the estimation of models with an a-priori unknown number of parameters such as buildings and have proven their worth for 3D modeling and reconstruction of cities. However, the generation and design of corresponding grammar rules is a laborious task and relies on expert knowledge. This thesis presents novel approaches for the reduction of this effort using advanced machine learning methods resulting in automatically learned sophisticated grammar rules. Indeed, the learning of a wide range of sophisticated rules, that reflect the variety and complexity, is a challenging task. This is especially the case if a simultaneous machine learning of building structures and the underlying aggregation hierarchies as well as the building parameters and the constraints among them for a semantic interpretation is expected. Thus, in this thesis, an incremental approach is followed. It separates the structure learning from the parameter distribution learning of building parts. Moreover, the so far procedural approaches with formal grammars are mostly rather convenient for the generation of virtual city models than for the reconstruction of existing buildings. To this end, Inductive Logic Programming (ILP) techniques are transferred and applied for the first time in the field of 3D building modeling. This enables the automatic learning of declarative logic programs, which are equivalent to attribute grammars and separate the representation of buildings and their parts from the reconstruction task. A stepwise bottom-up learning, starting from the smallest atomic features of a building part together with the semantic, topological and geometric constraints, is a key to a successful learning of a whole building part. Only few examples are sufficient to learn from precise as well as noisy observations. The learning from uncertain data is realized using probability density functions, decision trees and uncertain projective geometry. This enables the handling and modeling of uncertain topology and geometric reasoning taking noise into consideration. The uncertainty of models itself is also considered. Therefore, a novel method is developed for the learning of Weighted Attribute Context-Free Grammar (WACFG). On the one hand, the structure learning of façades – context-free part of the Grammar – is performed based on annotated derivation trees using specific Support Vector Machines (SVMs). The latter are able to derive probabilistic models from structured data and to predict a most likely tree regarding to given observations. On the other hand, to the best of my knowledge, Statistical Relational Learning (SRL), especially Markov Logic Networks (MLNs), are applied for the first time in order to learn building part (shape and location) parameters as well as the constraints among these parts. The use of SRL enables to take profit from the elegant logical relational description and to benefit from the efficiency of statistical inference methods. In order to model latent prior knowledge and exploit the architectural regularities of buildings, a novel method is developed for the automatic identification of translational as well as axial symmetries. For symmetry identification a supervised machine learning approach is followed based on an SVM classifier. Building upon the classification results, algorithms are designed for the representation of symmetries using context-free grammars from authoritative building footprints. In all steps the machine learning is performed based on real- world data such as 3D point clouds and building footprints. The handling with uncertainty and occlusions is assured. The presented methods have been successfully applied on real data. The belonging classification and reconstruction results are shown.Statistisches relationales Lernen von semantischen Modellen und Grammatikregeln für 3D Gebäuderekonstruktion aus 3D Punktwolken Formale Grammatiken eignen sich sehr gut zur Schätzung von Modellen mit a-priori unbekannter Anzahl von Parametern und haben sich daher als guter Ansatz zur Rekonstruktion von Städten mittels 3D Stadtmodellen bewährt. Der Entwurf und die Erstellung der dazugehörigen Grammatikregeln benötigt jedoch Expertenwissen und ist mit großem Aufwand verbunden. Im Rahmen dieser Arbeit wurden Verfahren entwickelt, die diesen Aufwand unter Zuhilfenahme von leistungsfähigen Techniken des maschinellen Lernens reduzieren und automatisches Lernen von Regeln ermöglichen. Das Lernen umfangreicher Grammatiken, die die Vielfalt und Komplexität der Gebäude und ihrer Bestandteile widerspiegeln, stellt eine herausfordernde Aufgabe dar. Dies ist insbesondere der Fall, wenn zur semantischen Interpretation sowohl das Lernen der Strukturen und Aggregationshierarchien als auch von Parametern der zu lernenden Objekte gleichzeitig statt finden soll. Aus diesem Grund wird hier ein inkrementeller Ansatz verfolgt, der das Lernen der Strukturen vom Lernen der Parameterverteilungen und Constraints zielführend voneinander trennt. Existierende prozedurale Ansätze mit formalen Grammatiken sind eher zur Generierung von synthetischen Stadtmodellen geeignet, aber nur bedingt zur Rekonstruktion existierender Gebäude nutzbar. Hierfür werden in dieser Schrift Techniken der Induktiven Logischen Programmierung (ILP) zum ersten Mal auf den Bereich der 3D Gebäudemodellierung übertragen. Dies führt zum Lernen deklarativer logischer Programme, die hinsichtlich ihrer Ausdrucksstärke mit attributierten Grammatiken gleichzusetzen sind und die Repräsentation der Gebäude von der Rekonstruktionsaufgabe trennen. Das Lernen von zuerst disaggregierten atomaren Bestandteilen sowie der semantischen, topologischen und geometrischen Beziehungen erwies sich als Schlüssel zum Lernen der Gesamtheit eines Gebäudeteils. Das Lernen erfolgte auf Basis einiger weniger sowohl präziser als auch verrauschter Beispielmodelle. Um das Letztere zu ermöglichen, wurde auf Wahrscheinlichkeitsdichteverteilungen, Entscheidungsbäumen und unsichere projektive Geometrie zurückgegriffen. Dies erlaubte den Umgang mit und die Modellierung von unsicheren topologischen Relationen sowie unscharfer Geometrie. Um die Unsicherheit der Modelle selbst abbilden zu können, wurde ein Verfahren zum Lernen Gewichteter Attributierter Kontextfreier Grammatiken (Weighted Attributed Context-Free Grammars, WACFG) entwickelt. Zum einen erfolgte das Lernen der Struktur von Fassaden –kontextfreier Anteil der Grammatik – aus annotierten Herleitungsbäumen mittels spezifischer Support Vektor Maschinen (SVMs), die in der Lage sind, probabilistische Modelle aus strukturierten Daten abzuleiten und zu prädizieren. Zum anderen wurden nach meinem besten Wissen Methoden des statistischen relationalen Lernens (SRL), insbesondere Markov Logic Networks (MLNs), erstmalig zum Lernen von Parametern von Gebäuden sowie von bestehenden Relationen und Constraints zwischen ihren Bestandteilen eingesetzt. Das Nutzen von SRL erlaubt es, die eleganten relationalen Beschreibungen der Logik mit effizienten Methoden der statistischen Inferenz zu verbinden. Um latentes Vorwissen zu modellieren und architekturelle Regelmäßigkeiten auszunutzen, ist ein Verfahren zur automatischen Erkennung von Translations- und Spiegelsymmetrien und deren Repräsentation mittels kontextfreier Grammatiken entwickelt worden. Hierfür wurde mittels überwachtem Lernen ein SVM-Klassifikator entwickelt und implementiert. Basierend darauf wurden Algorithmen zur Induktion von Grammatikregeln aus Grundrissdaten entworfen

Radial Icicle Tree (RIT): Node Separation and Area Constancy

Icicles and sunbursts are two commonly-used visual representations of trees. While icicle trees can map data values faithfully to rectangles of different sizes, often some rectangles are too narrow to be noticed easily. When an icicle tree is transformed into a sunburst tree, the width of each rectangle becomes the length of an annular sector that is usually longer than the original width. While sunburst trees alleviate the problem of narrow rectangles in icicle trees, it no longer maintains the consistency of size encoding. At different tree depths, nodes of the same data values are displayed in annular sections of different sizes in a sunburst tree, though they are represented by rectangles of the same size in an icicle tree. Furthermore, two nodes from different subtrees could sometimes appear as a single node in both icicle trees and sunburst trees. In this paper, we propose a new visual representation, referred to as \emph{radial icicle tree} (RIT), which transforms the rectangular bounding box of an icicle tree into a circle, circular sector, or annular sector while introducing gaps between nodes and maintaining area constancy for nodes of the same size. We applied the new visual design to several datasets. Both the analytical design process and user-centered evaluation have confirmed that this new design has improved the design of icicles and sunburst trees without introducing any relative demerit

Hierarchies and Ranks for Persistence Pairs

We develop a novel hierarchy for zero-dimensional persistence pairs, i.e., connected components, which is capable of capturing more fine-grained spatial relations between persistence pairs. Our work is motivated by a lack of spatial relationships between features in persistence diagrams, leading to a limited expressive power. We build upon a recently-introduced hierarchy of pairs in persistence diagrams that augments the pairing stored in persistence diagrams with information about which components merge. Our proposed hierarchy captures differences in branching structure. Moreover, we show how to use our hierarchy to measure the spatial stability of a pairing and we define a rank function for persistence pairs and demonstrate different applications.Comment: Topology-based Methods in Visualization 201

SoK: Diving into DAG-based Blockchain Systems

Blockchain plays an important role in cryptocurrency markets and technology services. However, limitations on high latency and low scalability retard their adoptions and applications in classic designs. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently re-build upper layer components including consensus, incentives, \textit{etc.} The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises the DAG technique. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of DAG-based blockchain systems. Through deconstructing open-sourced systems and reviewing academic researches, we conclude the main components and featured properties of systems, and provide the approach to establish a DAG. With this in hand, we analyze the security and performance of several leading systems, followed by discussions and comparisons with concurrent (scaling blockchain) techniques. We further identify open challenges to highlight the potentiality of DAG-based solutions and indicate their promising directions for future research.Comment: Full versio

A GUI For Defining Inductive Logic Programming Tasks For Novice Users

University of Minnesota M.S. thesis. March 2017. Major: Computer Science. Advisor: Richard Maclin. 1 computer file (PDF); vii, 64 pages.Inductive logic programming, which involves learning a solution to a problem where data is more naturally viewed as multiple tables with relationships between the tables, is an extremely powerful learning method. But these methods have suffered from the fact that very few are written in languages other than Prolog and because describing such problems is difficult. To describe an inductive logic programming problem the user needs to designate many tables and relationships and often provide some knowledge about the relationships in order for the techniques to work well. The goal of this thesis is to develop a Java-based Graphical User Interface (GUI) for novice users that will allow them to define ILP problems by connecting to an existing database and allowing users to define such a problem in an understandable way, perhaps with the assistance of data exploration techniques from the GUI

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

‘Translating Narrative into Code’ – Thoughts on a Technology-Centric Model of Digital Games as Programmable Media

The current surge of game studies literature demands a scrutinizing look at the solidifying positions within the German and international discourse and the opportunities of finding a common vocabulary and adequate set of analytical tools. In order to arrive at an integrative model of analysis, the intricate interplay of narrative and game mechanics (narratology vs. ludology) is considered from a comparative perspective and thus demystified. Consequently, a new model based on programming theory (object-oriented narrative) is proposed which takes into account elements of psychology, narratological concepts derived from film- and literature studies and cultural studies approaches leading to a ‘close reading’ of games. The article is based on the assumption that computer- and videogames recode the act of ‘reading’ games as texts into a mode of ‘text-processing’ and thereby fundamentally modify the parameters of our cultural appropriation and media literacy. The model proposed herein shall then be tentatively applied to the complex re-staging and re-examination of ‘the real’ in a wide range games from Wonderland to Enter the Matrix

Knowledge modeling of phishing emails

This dissertation investigates whether or not malicious phishing emails are detected better when a meaningful representation of the email bodies is available. The natural language processing theory of Ontological Semantics Technology is used for its ability to model the knowledge representation present in the email messages. Known good and phishing emails were analyzed and their meaning representations fed into machine learning binary classifiers. Unigram language models of the same emails were used as a baseline for comparing the performance of the meaningful data. The end results show how a binary classifier trained on meaningful data is better at detecting phishing emails than a unigram language model binary classifier at least using some of the selected machine learning algorithms