004 Datenverarbeitung; Informatik
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Through the increasing availability of access to the web, more and more interactions between people take place in online social networks, such as Twitter or Facebook, or sites where opinions can be exchanged. At the same time, knowledge is made openly available for many people, such as by the biggest collaborative encyclopedia Wikipedia and diverse information in Internet forums and on websites. These two kinds of networks - social networks and knowledge networks - are highly dynamic in the sense that the links that contain the important information about the relationships between people or the relations between knowledge items are frequently updated or changed. These changes follow particular structural patterns and characteristics that are far less random than expected.
The goal of this thesis is to predict three characteristic link patterns for the two network types of interest: the addition of new links, the removal of existing links and the presence of latent negative links. First, we show that the prediction of link removal is indeed a new and challenging problem. Even if the sociological literature suggests that reasons for the formation and resolution of ties are often complementary, we show that the two respective prediction problems are not. In particular, we show that the dynamics of new links and unlinks lead to the four link states of growth, decay, stability and instability. For knowledge networks we show that the prediction of link changes greatly benefits from the usage of temporal information; the timestamp of link creation and deletion events improves the prediction of future link changes. For that, we present and evaluate four temporal models that resemble different exploitation strategies. Focusing on directed social networks, we conceptualize and evaluate sociological constructs that explain the formation and dissolution of relationships between users. Measures based on information about past relationships are extremely valuable for predicting the dissolution of social ties. Hence, consistent for knowledge networks and social networks, temporal information in a network greatly improves the prediction quality. Turning again to social networks, we show that negative relationship information such as distrust or enmity can be predicted from positive known relationships in the network. This is particularly interesting in networks where users cannot label their relationships to other users as negative. For this scenario we show how latent negative relationships can be predicted.
In the recent years, Software Engineering research has shown the rise of interest in the empirical studies. Such studies are often based on empirical evidence derived from corpora - collections of software artifacts. While there are established forms of carrying out empirical research (experiments, case studies, surveys, etc.), the common task of preparing the underlying collection of software artifacts is typically addressed in ad hoc manner.
In this thesis, by means of a literature survey we show how frequently software engineering research employs software corpora and using a developed classification scheme we discuss their characteristics. Addressing the lack of methodology, we suggest a method of corpus (re-)engineering and apply it to an existing collection of Java projects.
We report two extensive empirical studies, where we perform a broad and diverse range of analyses on the language for privacy preferences (P3P) and on object-oriented application programming interfaces (APIs). In both cases, we are driven by the data at hand, by the corpus itself, discovering the actual usage of the languages.
The goal of this thesis is the development of methods for augmented image synthesis using 3D photo collections. 3D photo collections are representations of real scenes automatically generated from single photos and describe a scene as a set of images with known camera poses as well as a sparse point-based model of the scene geometry. The main goal is to perform a photo-realistic augmented image synthesis of real and virtual parts, where the real scene is provided as a 3D photo collection. Therefore, three main problems are addressed.
Since the photos may be represented in different device-specific RGB color spaces, a color characterization of the 3D photo collections is necessary to gain correct color information that is consistent with human perception. The proposed novel method automatically transforms all images into a common RGB color space and thereby simplifies color characterization of 3D photo collections.
As a main problem for augmented image synthesis, all environmental lighting has to be known in order to apply illumination to virtual parts that is consistent with the real portions shown in the photos. To solve this problem, two novel methods were developed to reconstruct the lighting from 3D photo collections.
In order to perform image synthesis for arbitrary views on the scene, an image-based approach was developed that generates new views in 3D photo collections making direct use of its point cloud. The novel method creates new views in real-time and allows free-navigation.
In conclusion, the proposed novel methods show that 3D photo collections are a useful representation for real scenes in Augmented Reality and they can be used to perform a realistic image synthesis of real and virtual portions.
The availability of digital cameras and the possibility to take photos at no cost lead to an increasing amount of digital photos online and on private computers. The pure amount of data makes approaches that support users in the administration of the photo necessary. As the automatic understanding of photo content is still an unsolved task, metadata is needed for supporting administrative tasks like search or photo work such as the generation of photo books. Meta-information textually describes the depicted scene or consists of information on how good or interesting a photo is.
In this thesis, an approach for creating meta-information without additional effort for the user is investigated. Eye tracking data is used to measure the human visual attention. This attention is analyzed with the objective of information creation in the form of metadata. The gaze paths of users working with photos are recorded, for example, while they are searching for photos or while they are just viewing photo collections.
Eye tracking hardware is developing fast within the last years. Because of falling prices for sensor hardware such as cameras and more competition on the eye tracker market, the prices are falling, and the usability is increasing. It can be assumed that eye tracking technology can soon be used in everyday devices such as laptops or mobile phones. The exploitation of data, recorded in the background while the user is performing daily tasks with photos, has great potential to generate information without additional effort for the users.
The first part of this work deals with the labeling of image region by means of gaze data for describing the depicted scenes in detail. Labeling takes place by assigning object names to specific photo regions. In total, three experiments were conducted for investigating the quality of these assignments in different contexts. In the first experiment, users decided whether a given object can be seen on a photo by pressing a button. In the second study, participants searched for specific photos in an image search application. In the third experiment, gaze data was collected from users playing a game with the task to classify photos regarding given categories. The results of the experiments showed that gaze-based region labeling outperforms baseline approaches in various contexts. In the second part, most important photos in a collection of photos are identified by means of visual attention for the creation of individual photo selections. Users freely viewed photos of a collection without any specific instruction on what to fixate, while their gaze paths were recorded. By comparing gaze-based and baseline photo selections to manually created selections, the worth of eye tracking data in the identification of important photos is shown. In the analysis of the data, the characteristics of gaze data has to be considered, for example, inaccurate and ambiguous data. The aggregation of gaze data, collected from several users, is one suggested approach for dealing with this kind of data.
The results of the performed experiments show the value of gaze data as source of information. It allows to benefit from human abilities where algorithms still have problems to perform satisfyingly.
German politicians have identified a need for greater citizen involvement in decision-making than in the past, as confirmed by a recent German parliamentarians study ("DEUPAS"). As in other forms of social interactions, the Internet provides significant potential to serve as the digital interface between citizens and decision-makers: in the recent past, dedicated electronic participation ("e-participation") platforms (e.g. dedicated websites) have been provided by politicians and governments in an attempt to gather citizens" feedback and comment on a particular issue or subject. Some of these have been successful, but a large proportion of them are grossly under-used " often only small numbers of citizens use them. Over the same time period, enthusiasm of Society for social networks has increased and is now commonplace. Many citizens use social networks such as Facebook and Twitter for all kinds of purposes, and in some cases to discuss political issues.
Social networks are therefore obviously attractive to politicians " from local government to federal agencies, politicians have integrated social media into their daily work. However, there is a significant challenge regarding the usefulness of social networks. The problem is the continuous increase in digital information: social networks contain vast amounts of information, and it is impossible for a human to manually filter the relevant information from the irrelevant (so-called "information overload"). Even using the search tools provided by social networks, it is still a huge task for a human to determine meanings and themes from the multitude of search results. New technologies and concepts have been proposed to provide summaries of masses of information through lexical analysis of social media messages, and therefore they promise an easy and quick overview of the information.
This thesis examines the relevance of these analyses" results, for the use in everyday political life, with the emphasis on the social networks Facebook and Twitter as data sources. Here we make use of the WeGov Toolbox and its analysis components that were developed during the EU project WeGov. The assessment has been performed in consultation with actual policy-makers from different levels of German government: policy-makers from the German Federal Parliament, the State Parliament North Rhine-Westphalia, the State Chancellery of the Saarland and the cities of Cologne and Kempten all took part in the study. Our method was to execute the analyses on data collected from Facebook and Twitter, and present the results to the policy-makers, who would then evaluate them using a mixture of qualitative methods.
The responses of the participants have provided us with some useful conclusions:
1) None of the participants believe that e-participation is possible in this way. But participants confirm that "citizen-friendliness" can be supported by this approach.
2) The most likely users for the summarisation tools are those who have experience with social networks, but are not "power users". The reason being is that "power users" already knew the relevant information provided by analysis tools. But without any experiences for social networks it is hard to interpret the analysis results the right way.
3) The evaluation has considered geographical aspects, and related this to e.g. a politician- constituency as a local area of social networks. Comparing the rural to the urban areas, it is shown that the amount of relevant political information in the rural areas is low. While the proportion of publicly available information in urban areas is relatively high, the proportion in the rural areas is much lower.
The findings that result from the engagement with policy-makers will be systematically surveyed and validated within this thesis.
Web 2.0 provides technologies for online collaboration of users as well as the creation, publication and sharing of user-generated contents in an interactive way. Twitter, CNET, CiteSeerX, etc. are examples of Web 2.0 platforms which facilitate users in these activities and are viewed as rich sources of information. In the platforms mentioned as examples, users can participate in discussions, comment others, provide feedback on various issues, publish articles and write blogs, thereby producing a high volume of unstructured data which at the same time leads to an information overload. To satisfy various types of human information needs arising from the purpose and nature of the platforms requires methods for appropriate aggregation and automatic analysis of this unstructured data. In this thesis, we propose methods which attempt to overcome the problem of information overload and help in satisfying user information needs in three scenarios.
To this end, first we look at two of the main challenges of sparsity and content quality in Twitter and how these challenges can influence standard retrieval models. We analyze and identify Twitter content features that reflect high quality information. Based on this analysis we introduce the concept of "interestingness" as a static quality measure. We empirically show that our proposed measure helps in retrieving and filtering high quality information in Twitter. Our second contribution relates to the content diversification problem in a collaborative social environment, where the motive of the end user is to gain a comprehensive overview of the pros and cons of a discussion track which results from social collaboration of the people. For this purpose, we develop the FREuD approach which aims at solving the content diversification problem by combining latent semantic analysis with sentiment estimation approaches. Our evaluation results show that the FREuD approach provides a representative overview of sub-topics and aspects of discussions, characteristic user sentiments under different aspects, and reasons expressed by different opponents. Our third contribution presents a novel probabilistic Author-Topic-Time model, which aims at mining topical trends and user interests from social media. Our approach solves this problem by means of Bayesian modeling of relations between authors, latent topics and temporal information. We present results of application of the model to the scientific publication datasets from CiteSeerX showing improved semantically cohesive topic detection and capturing shifts in authors" interest in relation to topic evolution.
Diffusion imaging captures the movement of water molecules in tissue by applying varying gradient fields in a magnetic resonance imaging (MRI)-based setting. It poses a crucial contribution to in vivo examinations of neuronal connections: The local diffusion profile enables inference of the position and orientation of fiber pathways. Diffusion imaging is a significant technique for fundamental neuroscience, in which pathways connecting cortical activation zones are examined, and for neurosurgical planning, where fiber reconstructions are considered as intervention related risk structures.
Diffusion tensor imaging (DTI) is currently applied in clinical environments in order to model the MRI signal due to its fast acquisition and reconstruction time. However, the inability of DTI to model complex intra-voxel diffusion distributions gave rise to an advanced reconstruction scheme which is known as high angular resolution diffusion imaging (HARDI). HARDI received increasing interest in neuroscience due to its potential to provide a more accurate view of pathway configurations in the human brain.
In order to fully exploit the advantages of HARDI over DTI, advanced fiber reconstructions and visualizations are required. This work presents novel approaches contributing to current research in the field of diffusion image processing and visualization. Diffusion classification, tractography, and visualizations approaches were designed to enable a meaningful exploration of neuronal connections as well as their constitution. Furthermore, an interactive neurosurgical planning tool with consideration of neuronal pathways was developed.
The research results in this work provide an enhanced and task-related insight into neuronal connections for neuroscientists as well as neurosurgeons and contribute to the implementation of HARDI in clinical environments.