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2012


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Experimentelle Induktion beeinträchtigter Aufmerksamkeit im Kontext des seductive detail Effekts

Wirzberger, M.

University of Hagen, 2012 (thesis)

Abstract
Die vorliegende Arbeit untersucht ausgehend von der Cognitive Theory of Multimedia Learning (CTML) einen aufmerksamkeitsbezogenen Erklärungsansatz für den seductive detail Effekt. Dieser Effekt resultiert aus dem Einfügen interessanter, jedoch irrelevanter Informationen in einen Lerntext, die die Lernleistung beeinträchtigen. Im Besonderen steht hier die Hypothese im Fokus, dass sich seductive details stärker auswirken, wenn bereits eine Beeinträchtigung der Aufmerksamkeit vorliegt. Im Rahmen einer experimentellen Erhebung mit 53 Studierenden wurde anhand eines 2x2-faktoriellen, multivariaten Designs die Anwesenheit von seductive details (durch seduktive Textpassagen), sowie beeinträchtigter Aufmerksamkeit (durch die Einblendung ablenkender Systemmitteilungen), gezielt manipuliert und deren Effekt auf die Behaltens- und Verstehensleistung, sowie die Lernzeit erfasst. In den Analysen zeigte sich eine signifikante Verlängerung der Lernzeit durch das Einfügen seduktiver Textpassagen, und darüber hinaus wurde auch ein moderierender Einfluss des bestehenden Aufmerksamkeitsniveaus deutlich. Weder die Behaltens- noch die Verstehensleistung verringerte sich jedoch signifikant durch das Hinzufügen von seductive details oder die Induktion beeinträchtigter Aufmerksamkeit, und auch eine signifikante Wechselwirkung zwischen beiden Aspekten wurde nicht deutlich. Daher wird abschließend die Relevanz komplexer statistischer Analyseverfahren zur weiteren Erhellung der zugrundeliegenden Wirkmechanismen diskutiert.

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DOI [BibTex]


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The Playful Machine - Theoretical Foundation and Practical Realization of Self-Organizing Robots

Der, R., Martius, G.

Springer, Berlin Heidelberg, 2012 (book)

Abstract
Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complex machines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? The book provides answers by developing a general principle---homeokinesis, the dynamical symbiosis between brain, body, and environment---that is shown to drive robots to self-determined, individual development in a playful and obviously embodiment-related way: a dog-like robot starts playing with a barrier, eventually jumping or climbing over it; a snakebot develops coiling and jumping modes; humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarios when encountering an opponent. The book also develops guided self-organization, a new method that helps to make the playful machines fit for fulfilling tasks in the real world.

al

link (url) [BibTex]


Consumer Depth Cameras for Computer Vision - Research Topics and Applications
Consumer Depth Cameras for Computer Vision - Research Topics and Applications

Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K.

Advances in Computer Vision and Pattern Recognition, Springer, 2012 (book)

ps

workshop publisher's site [BibTex]

workshop publisher's site [BibTex]

2007


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Predicting Structured Data

Bakir, G., Hofmann, T., Schölkopf, B., Smola, A., Taskar, B., Vishwanathan, S.

pages: 360, Advances in neural information processing systems, MIT Press, Cambridge, MA, USA, September 2007 (book)

Abstract
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning’s greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field.

ei

Web [BibTex]

2007


Web [BibTex]


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Large-Scale Kernel Machines

Bottou, L., Chapelle, O., DeCoste, D., Weston, J.

pages: 416, Neural Information Processing Series, MIT Press, Cambridge, MA, USA, September 2007 (book)

Abstract
Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.

ei

Web [BibTex]

Web [BibTex]

2003


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Magnetism and the Microstructure of Ferromagnetic Solids

Kronmüller, H., Fähnle, M.

pages: 432 p., 1st ed., Cambridge University Press, Cambridge, 2003 (book)

mms

[BibTex]

2003


[BibTex]