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PhD thesis abstracts
September 2011
Doreen BoehnstedtSemantic Tagging for Managing Web-based Learning Resources: Models, Methods and a Plattform for Supporting Resource-based Learning
However, self-directed Resource-based Learning also poses many challenges to learners. First, digital resources on the Web are usually not didactically prepared and therefore are not intended to be used as learning materials. In addition, the relevant information is often distributed across many different websites. Further, there is already a very large but still rapidly increasing amount of information available on the Web, which can lead to information overload. In the scenario of self-directed learning considered here, there is no teacher who structures the learning process. Therefore, learners have to independently determine their information needs and plan their proceeding. They have to identify, annotate and organize relevant resources for future use. This makes an appropriate management of resources necessary. However, the majority of learners is unsatisfied with the currently available possibilities for the organization of Web resources. The goal of this thesis is therefore the design and development of a tool to support learners in Resource-based Learning. In particular, the management of resources should be supported and hence challenges mentioned above are addressed. Self-directed Resource-based Learning requires a personal information and knowledge management by the learners. In literature, several models for managing information and knowledge in organizational and personal scopes exist. For self-directed Resource-based Learning such a model is missing so far. Therefore, a model for Resource-based Learning is developed based on the existing models and on a questionnaire survey conducted in the context of this thesis. This model encompasses several process steps that should be supported by the tool. The management of resources necessitates the learners to appropriately store the resources, such as based on topic of interest or task to be executed. Tagging is a simple and accepted way to manage any resource on the Web, but its power of expression is restricted. Other forms of resource management can be found in the area of formal knowledge organization (e.g. modeling of a semantic network), however, expert knowledge is usually required to build a semantic network. As a basis for the tool that is developed in the context of this work, therefore, a combination of both forms is proposed, i.e. a semantic network that is created and expanded by the learners using tagging. Core components of this network are resources and tags. Additionally, a learner is able to assign a type to each tag. Therefore, the information whether the tag is e.g. a topic or task can be stored. As part of this thesis, the types of tags that are necessary for the scenario of Resource-based Learning have been analyzed and evaluated. Furthermore, an algorithm for automatic detection of these tag types is presented, as such an algorithm can reduce the manual maintenance effort for the management of resources. The evaluation of various corpora shows that the knowledge-based algorithm can classify a tag already during the tagging process with an accuracy which is sufficient for the scenario. Based on the developed model of Resource-based Learning and its requirements for the management of resources, different tools and systems are analyzed with regard to their support of Resource-based Learning. None of the related tools fulfill the requirements appropriately. Therefore, on the basis of the model's process steps and the derived functional requirements a concept for a supporting tool is developed. Based on the technical requirements, a system is designed, consisting of a browser add-on, a backend for the management of the knowledge networks and a web-based frontend. The tool is implemented and evaluated in user studies eventually. The user studies conducted in this work show that the extended form of tagging, based on tag types, is well accepted and allows for appropriate management of resources. Furthermore, the studies show that the implemented tool addresses the challenges of self-directed Resource-based Learning adequately. The present work thus creates a basis for optimizing the approach to self-directed interaction with resources in order to meet an information need.
Jun WangSemi-Supervised Learning for Scalable and Robust Visual Search
Graph-based approaches have emerged as methods of choice for general semi-supervised tasks when no parametric information is available about the data distribution. It treats both labeled and unlabeled samples as vertices in a graph and then instantiates pairwise edges between these vertices to capture affinity between the corresponding samples. A quadratic regularization framework has been widely used for label prediction over such graphs. However, most of the existing graph-based semi-supervised learning methods are sensitive to the graph construction process and the initial labels. We propose a new bivariate graph transduction formulation and an efficient solution via an alternating minimization procedure. Based on this bivariate framework, we also develop new methods to filter unreliable and noisy labels. Extensive experiments over diverse benchmark datasets demonstrate the superior performance of our proposed methods. However, graph-based approaches suffer from the critical bottleneck in scalability since graph construction requires a quadratic complexity and the inference procedure costs even more. The widely used graph construction method relies on nearest neighbor search, which is prohibitive for large-scale applications. In addition, most large-scale visual search problems involve handling high-dimensional visual descriptors, thereby causing another challenge in excessive storage requirement. To handle the scalability issue of both computation and storage, the second part of the thesis focuses on efficient techniques for conducting approximate nearest neighbor (ANN) search, which is key to many machine learning algorithms, including graph-based semi-supervised learning and clustering. Specifically, we propose Semi-Supervised Hashing (SSH) methods that leverage semantic similarity over a small set of labeled data while preventing overfitting. We derive a rigorous formulation in which a supervised term minimizes the empirical errors on the labeled data and an unsupervised term provides effective regularization by maximizing variance and independence of individual bits. Experiments on several large datasets demonstrate the clear performance gain over several state-of-the-art methods without significant increase of the computational cost. The main contributions of the thesis include the following. 1) a bivariate formulation for graph-based semi-supervised learning with an efficient solution by alternating optimization; b) theoretic analysis from the view of graph cut for the bivariate optimization procedure; c) novel applications of the proposed techniques, such as interactive image retrieval, automatic re-ranking for text based image search, and a brain computer interface (BCI) for image retrieval. 2) a rigorous semi-supervised paradigm for hash functions learning with a tradeoff between empirical fitness on pair-wise label consistence and an information-theoretic regularizer; b) several efficient solutions for deriving semi-supervised hash functions, including an orthogonal solution using eigen-decomposition, a revised strategy for learning non-orthogonal hash functions, a sequential learning algorithm to derive boosted hash functions, and an extension to unsupervised cases by using pseudo labels. Two parts of the thesis - bivariate graph transduction and semi-supervised hashing - are complimentary and can be combined to achieve significant performance improvement in both speed and accuracy. Hash methods can help build sparse graphs in a linear time fashion and greatly reduce the data size, but they lack sufficient accuracy. Graph-based methods provide unique capabilities to handle non-linear data structures with noisy labels but suffer from high computational complexity. The synergistic combination of the two offers great potential for advancing the state-of-the-art in large-scale visual search and many other applications.
Philipp SchollSemantic and Structural Analysis of Web-based Learning Resources - Supporting Self-directed Resource-based Learning
Wanmin WuHuman-centric Control of Video Functions and Underlying Resources in 3D Tele-immersive Systems
This thesis proposes a novel, comprehensive, human-centric framework for improving the qualities of 3DTI throughout its video function pipeline. We make three major contributions at different phases of the pipeline. At the sending side, we develop an intra-stream data adaptation scheme that reduces level-of-details within each stream without users being aware of it. This human-centric approach exploits limitations of human vision, and excludes details that are imperceptible. It effectively alleviates the data load for computation-intensive operations, thus improves the temporal efficiency of the systems. Yet even with intra-stream data reduced, spatial efficiency is still a problem due to the multi-stream/multi-site nature of 3DTI collaboration. We thus develop an inter-stream data adaptation scheme at the networking phase to reduce the number of streams with minimal disruption to the visual quality. This human-centric approach prioritizes streams based on user views and excludes less important streams from transmission. It considerably reduces the data load for networking, and thus enhances the spatial resource efficiency. The above two approaches (level-of-details reduction within a video stream and view-based differentiation among streams) work seamlessly together to bring both temporal and spatial resource demands under control, and prove to improve various qualities of the systems. Finally, at the receiving side, we take a holistic approach to study the ``quality'' concept in 3DTI environments. Our human-centric quality framework focuses on the Quality-of-Experience (QoE) concept that models user's perceptions, emotions, performances, etc. It investigates how the traditional Quality-of-Service (QoS) impacts QoE, and reveals how QoS should be improved for the best user experience. This thesis essentially demonstrates the importance of bringing human-awareness into the design, execution, and evaluation of the complex resource-constrained 3DTI environments.
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