Yavaa: supporting data workflows from discovery to visualization

Recent years have witness an increasing number of data silos being opened up both within organizations and to the general public: Scientists publish their raw data as supplements to articles or even standalone artifacts to enable others to verify and extend their work. Governments pass laws to open up formerly protected data treasures to improve accountability and transparency as well as to enable new business ideas based on this public good. Even companies share structured information about their products and services to advertise their use and thus increase revenue. Exploiting this wealth of information holds many challenges for users, though. Oftentimes data is provided as tables whose sheer endless rows of daunting numbers are barely accessible. InfoVis can mitigate this gap. However, offered visualization options are generally very limited and next to no support is given in applying any of them. The same holds true for data wrangling. Only very few options to adjust the data to the current needs and barely any protection are in place to prevent even the most obvious mistakes. When it comes to data from multiple providers, the situation gets even bleaker. Only recently tools emerged to search for datasets across institutional borders reasonably. Easy-to-use ways to combine these datasets are still missing, though. Finally, results generally lack proper documentation of their provenance. So even the most compelling visualizations can be called into question when their coming about remains unclear. The foundations for a vivid exchange and exploitation of open data are set, but the barrier of entry remains relatively high, especially for non-expert users. This thesis aims to lower that barrier by providing tools and assistance, reducing the amount of prior experience and skills required. It covers the whole workflow ranging from identifying proper datasets, over possible transformations, up until the export of the result in the form of suitable visualizations

Improving Usability in Procedural Modeling

This work presents new approaches and algorithms for procedural modeling geared towards user convenience and improving usability, in order to increase artists’ productivity. Procedural models create geometry for 3D models from sets of rules. Existing approaches that allow to model trees, buildings, and terrain are reviewed and possible improvements are discussed. A new visual programming language for procedural modeling is discussed, where the user connects operators to visual programs called model graphs. These operators create geometry with textures, assign or evaluate variables or control the sequence of operations. When the user moves control points using the mouse in 3D space, the model graph is executed to change the geometry interactively. Thus, model graphs combine the creativity of freehand modeling with the power of programmed modeling while displaying the program structure more clearly than textbased approaches. Usability is increased as a result of these advantages. Also, an interactive editor for botanical trees is demonstrated. In contrast to previous tree modeling systems, we propose linking rules, parameters and geometry to semantic entities. This has the advantage that problems of associating parameters and instances are completely avoided. When an entity is clicked in the viewport, its parameters are displayed immediately, changes are applied to selected entities, and viewport editing operations are reflected in the parameter set. Furthermore, we store the entities in a hierarchical data structure and allow the user to activate recursive traversal via selection options for all editing operations. The user may choose to apply viewport or parameter changes to a single entity or many entities at once, and only the geometry for the affected entities needs to be updated. The proposed user interface simplifies the modeling process and increases productivity. Interactive editing approaches for 3D models often allow more precise control over a model than a global set of parameters that is used to generate a shape. However, usually scripted procedural modeling generates shapes directly from a fixed set of parameters, and interactive editing mostly uses a fixed set of tools. We propose to use scripts not only to generate models, but also for manipulating the models. A base script would set up the state of an object, and tool scripts would modify that state. The base script and the tool scripts generate geometry when necessary. Together, such a collection of scripts forms a template, and templates can be created for various types of objects. We examine how templates simplify the procedural modeling workflow by allowing for editing operations that are context-sensitive, flexible and powerful at the same time. Many algorithms have been published that produce geometry for fictional landscapes. There are algorithms which produce terrain with minimal setup time, allowing to adapt the level of detail as the user zooms into the landscape. However, these approaches lack plausible river networks, and algorithms that create eroded terrain with river networks require a user to supervise creation and minutes or hours of computation. In contrast to that, this work demonstrates an algorithm that creates terrain with plausible river networks and adaptive level of detail with no more than a few seconds of preprocessing. While the system can be configured using parameters, this text focuses on the algorithm that produces the rivers. However, integrating more tools for user-controlled editing of terrain would be possible.Verbesserung der Usability bei prozeduraler Modellierung Ziel der vorliegenden Arbeit ist es, prozedurale Modellierung durch neue neue Ansätze und Algorithmen einfacher, bequemer und anwendungsfreundlicher zu machen, und damit die Produktivität der Künstler zu erhöhen. Diese Anforderungen werden häufig unter dem Stichwort Usability zusammengefasst. Prozedurale Modelle spezifizieren 3D-Modelle über Regeln. Existierende Ansätze für Bäume, Gebäude und Terrain werden untersucht und es werden mögliche Verbesserungen diskutiert. Eine neue visuelle Programmiersprache für prozedurale Modelle wird vorgestellt, bei der Operatoren zu Modellgraphen verschaltet werden. Die Operatoren erzeugen texturierte Geometrie, weisen Variablen zu und werten sie aus, oder sie steuern den Ablauf der Operationen. Wenn der Benutzer Kontrollpunkte im Viewport mit der Maus verschiebt, wird der Modellgraph ausgeführt, um interaktiv neue Geometrie für das Modell zu erzeugen. Modellgraphen kombinieren die kreativen Möglichkeiten des freihändigen Editierens mit der Mächtigkeit der prozeduralen Modellierung. Darüber hinaus sind Modellgraphen eine visuelle Programmiersprache und stellen die Struktur der Algorithmen deutlicher dar als textbasierte Programmiersprachen. Als Resultat dieser Verbesserungen erhöht sich die Usability. Ein interaktiver Editor für botanische Bäume wird ebenfalls vorgestellt. Im Gegensatz zu früheren Ansätzen schlagen wir vor, Regeln, Parameter und Geometrie zu semantischen Entitäten zu verschmelzen. Auf diese Weise werden Zuordnungsprobleme zwischen Parametern und deren Instanzen komplett vermieden. Wenn im Viewport eine Instanz angeklickt wird, werden sofort ihre Parameter angezeigt, alle Änderungen wirken sich direkt auf die betroffenen Instanzen aus, und Änderungen im Viewport werden sofort in den Parametern reflektiert. Darüber hinaus werden die Entitäten in einer hierarchischen Datenstruktur gespeichert und alle Änderungen können rekursiv auf der Hierarchie ausgeführt werden. Dem Benutzer werden Selektionsoptionen zur Verfügung gestellt, über die er Änderungen an den Parametern oder Änderungen im Viewport an einzelnen oder vielen Instanzen gleichzeitig vornehmen kann. Anschließend muss das System nur die Geometrie der betroffenen Instanzen aktualisieren. Auch hier ist das Ziel, das User Interface möglichst an den Bedürfnissen des Benutzers auszurichten, um Vereinfachungen und eine Erhöhung der Produktivität zu erreichen. Interaktive Editieransätze für 3D-Modelle erlauben häufig eine präzisere Kontrolle über ein Modell als ein globaler Parametersatz, der für die Erzeugung des Modells genutzt wird. Trotzdem erzeugen prozedurale Modellierskripte ihre Modelle meist direkt aus einem festen Parametersatz, während interaktive Tools meist mit hartkodierten Operationen arbeiten. Wir schlagen vor, Skripte nicht nur zur Erzeugung der Modelle zu verwenden, sondern auch um die erzeugten Modelle zu editieren. Ein Basisskript soll die Statusinformationen eines Objekts anlegen, während weitere Skripte diesen Status verändern und passende Geometrie erzeugen. Diese Skripte bilden dann ein Template zum Erzeugen einer Klasse von Objekten. Verschiedene Objekttypen können jeweils ihr eigenes Template haben. Wir zeigen, wie Templates den Workflow mit prozeduralen Modellen vereinfachen können, indem Operationen geschaffen werden, die gleichzeitig kontext-sensitiv, mächtig und flexibel sind. Es existiert eine Reihe von Verfahren, um Geometrie für synthetische Landschaften zu erzeugen. Ein Teil der Algorithmen erzeugt Geometrie mit minimaler Vorberechnung und erlaubt es, den Detailgrad der Landschaft interaktiv an die Perspektive anzupassen. Leider fehlen den so erzeugten Landschaften plausible Flussnetze. Algorithmen, die erodiertes Terrain mit Flussnetzen erzeugen, müssen aufwendig vom Benutzer überwacht werden und brauchen Minuten oder Stunden Rechenzeit. Im Gegensatz dazu stellen wir einen Algorithmus vor, der plausible Flussnetze erzeugt, während sich der Betrachter interaktiv durch die Szene bewegt. Das System kann über Parameter gesteuert werden, aber der Fokus liegt auf dem Algorithmus zur Erzeugung der Flüsse. Dennoch wäre es möglich, Tools zum benutzergesteuerten Editieren von Terrain zu integrieren

Coordinating knowledge to improve optical music recognition

Optical Music Recognition (OMR) is the process of automatically processing and understanding an image of a music score. This process involves various distinct phases to transform the image into primitive shapes, musical objects, and ultimately into a syntactic model representing the music's semantics. In general, OMR systems have performed these tasks in a linear sequence, so that the output of one component is the input to the next. However, this means that processing errors that occur in one of the tasks propagate through the system, and often when the error is eventually detected it is too late to reconsider the decisions leading to the incorrect classification or information. This thesis describes how OMR can be improved by modifying the recognition process from a sequence of linear tasks to a collection of modules that coordinate the information extracted from the data. Methods for_ data representation and controlling the system's flow of execution are investigated, and a practical implementation of such a system is described. This system has a message-passing design for providing contextual information from one module to another, such as suggesting possible classifications for an object. These messages are used to aid decision-making and to correct faulty decisions. This helps the system to adapt to a particular score while processing the image, increasing accuracy. This system is designed to aid in the research and evaluation of algorithms to achieve the above aims; therefore it is straightforward to modify various aspects of the system's behaviour, such as adding support for different music symbols. Examining the implemented system's behaviour clearly shows that this coordinated approach can correct many errors and can even identify some objects by only using syntactic information, based on the surrounding objects

Evaluating Parsers with Dependency Constraints

Many syntactic parsers now score over 90% on English in-domain evaluation, but the remaining errors have been challenging to address and difficult to quantify. Standard parsing metrics provide a consistent basis for comparison between parsers, but do not illuminate what errors remain to be addressed. This thesis develops a constraint-based evaluation for dependency and Combinatory Categorial Grammar (CCG) parsers to address this deficiency. We examine the constrained and cascading impact, representing the direct and indirect effects of errors on parsing accuracy. This identifies errors that are the underlying source of problems in parses, compared to those which are a consequence of those problems. Kummerfeld et al. (2012) propose a static post-parsing analysis to categorise groups of errors into abstract classes, but this cannot account for cascading changes resulting from repairing errors, or limitations which may prevent the parser from applying a repair. In contrast, our technique is based on enforcing the presence of certain dependencies during parsing, whilst allowing the parser to choose the remainder of the analysis according to its grammar and model. We draw constraints for this process from gold-standard annotated corpora, grouping them into abstract error classes such as NP attachment, PP attachment, and clause attachment. By applying constraints from each error class in turn, we can examine how parsers respond when forced to correctly analyse each class. We show how to apply dependency constraints in three parsers: the graph-based MSTParser (McDonald and Pereira, 2006) and the transition-based ZPar (Zhang and Clark, 2011b) dependency parsers, and the C&C CCG parser (Clark and Curran, 2007b). Each is widely-used and influential in the field, and each generates some form of predicate-argument dependencies. We compare the parsers, identifying common sources of error, and differences in the distribution of errors between constrained and cascaded impact. Our work allows us to contrast the implementations of each parser, and how they respond to constraint application. Using our analysis, we experiment with new features for dependency parsing, which encode the frequency of proposed arcs in large-scale corpora derived from scanned books. These features are inspired by and extend on the work of Bansal and Klein (2011). We target these features at the most notable errors, and show how they address some, but not all of the difficult attachments across newswire and web text. CCG parsing is particularly challenging, as different derivations do not always generate different dependencies. We develop dependency hashing to address semantically redundant parses in n-best CCG parsing, and demonstrate its necessity and effectiveness. Dependency hashing substantially improves the diversity of n-best CCG parses, and improves a CCG reranker when used for creating training and test data. We show the intricacies of applying constraints to C&C, and describe instances where applying constraints causes the parser to produce a worse analysis. These results illustrate how algorithms which are relatively straightforward for constituency and dependency parsers are non-trivial to implement in CCG. This work has explored dependencies as constraints in dependency and CCG parsing. We have shown how dependency hashing can efficiently eliminate semantically redundant CCG n-best parses, and presented a new evaluation framework based on enforcing the presence of dependencies in the output of the parser. By otherwise allowing the parser to proceed as it would have, we avoid the assumptions inherent in other work. We hope this work will provide insights into the remaining errors in parsing, and target efforts to address those errors, creating better syntactic analysis for downstream applications

Enhancing the Communication of Law: a cross-disciplinary investigation applying information technology

Law is pervasive in culture. It is a form of communication between government and citizens. When effective, it is a tool of government policy. If poorly designed,law results in unnecessary costs to society. Impediments to understanding of the law limits and distorts democratic participation. Yet, historically, the law has been inaccessible to most. Thus enhancing the communication of law is an important and standing problem. Much work has been done (for example through the plain language movement) to improve the communication of law. Nonetheless, the law remains largely unreadable to non-legal users. This thesis applies information technology to investigate and enhance the communication of law. To this end, this thesis focusses on four main areas.To improve the readability of law, it must be better described as a form of language. Corpus linguistics is applied for this purpose. A linguistic description of contract language arose from this work, which, along with the corpus itself, has been made available to the research community. The thesis also describes work for the automatic classification of text in legal contracts by legal function.Reliable measures for the readability of law are needed, but they do not exist. To develop such measures, gold standard data is needed to evaluate possible measures.To create this gold standard data, the research engaged citizen scientists, in the form of the online “crowd”. However, methods for creating and using such user assessments for readability are rudimentary. The research therefore investigated,developed and applied a number of methods for collecting user ratings of readability in an online environment. Also, the research applied machine learning to investigate and identify linguistic factors that are specifically associated with language difficulty of legislative sentences. This resulted in recommendations for improving legislative readability. A parallel line of investigation concerned the application of visualization to enhance the communication of law. Visualization engages human visual perception and its parallel processing capacities for the communication of law. The research applied computational tools: natural language processing, graph characteristics and data driven algorithms. It resulted in prototype tools for automatically visualizing definition networks and automating the visualization of selected contract clauses. Also, the work has fostered an investigation of the nature of law itself. A “law as” framework is used to query the nature of law and illuminate law in new ways. The framework is re-assessed as a tool for the experimental investigation of law. This results in an enhanced description of law, applying a number of investigatory frames:law; communication; document; information; computation; design and complex systems theory. It also provides a contrastive study with traditional theories of law - demonstrating how traditional theories can be extended in the light of these multidisciplinary results. In sum, this thesis reports a body of work advancing the existing knowledge base and state of the art in respect of application of computational techniques to enhancing the communication of law