Mobile Games for Learning

Today, pupils at the age of 15 have spent their entire life surrounded by and interacting with diverse forms of computers. It is a routine part of their day-to-day life and by now computer-literacy is common at very early age. Over the past five years, technology for teens has become predominantly mobile and ubiquitous within every aspect of their lives. To them, being online is an implicitness. In Germany, 88% of youth aged between 12-19 years own a smartphone and about 20% use the Internet via tablets. Meanwhile, more and more young learners bring their devices into the classroom and pupils increasingly demand for innovative and motivating learning scenarios that strongly respond to their habits of using media. With this development, a shift of paradigm is slowly under way with regard to the use of mobile technology in education. By now, a large body of literature exists, that reports concepts, use-cases and practical studies for effectively using technology in education. Within this field, a steadily growing body of research has developed that especially examines the use of digital games as instructional strategy. The core concern of this thesis is the design of mobile games for learning. The conditions and requirements that are vital in order to make mobile games suitable and effective for learning environments are investigated. The base for exploration is the pattern approach as an established form of templates that provide solutions for recurrent problems. Building on this acknowledged form of exchanging and re-using knowledge, patterns for game design are used to classify the many gameplay rules and mechanisms in existence. This research draws upon pattern descriptions to analyze learning game concepts and to abstract possible relationships between gameplay patterns and learning outcomes. The linkages that surface are the starting bases for a series of game design concepts and their implementations are subsequently evaluated with regard to learning outcomes. The findings and resulting knowledge from this research is made accessible by way of implications and recommendations for future design decisions.

Canine keratinocytes upregulate type I interferons and proinflammatory cytokines in response to poly(dA:dT) but not to canine papillomavirus.

Papillomaviruses (PV) are double stranded (ds) DNA viruses that infect epithelial cells within the skin or mucosa, most often causing benign neoplasms that spontaneously regress. The immune system plays a key role in the defense against PVs. Since these viruses infect keratinocytes, we wanted to investigate the role of the keratinocyte in initiating an immune response to canine papillomavirus-2 (CPV-2) in the dog. Keratinocytes express a variety of pattern recognition receptors (PRR) to distinguish different cutaneous pathogens and initiate an immune response. We examined the mRNA expression patterns for several recently described cytosolic nucleic acid sensing PRRs in canine monolayer keratinocyte cultures using quantitative reverse transcription-polymerase chain reaction. Unstimulated normal cells were found to express mRNA for melanoma differentiation associated gene 5 (MDA5), retinoic acid-inducible gene I (RIG-I), DNA-dependent activation of interferon regulatory factors, leucine rich repeat flightless interacting protein 1, and interferon inducible gene 16 (IFI16), as well as their adaptor molecules myeloid differentiation primary response gene 88, interferon-β promoter stimulator 1, and endoplasmic reticulum-resident transmembrane protein stimulator of interferon genes. When stimulated with synthetic dsDNA [poly(dA:dT)] or dsRNA [poly(I:C)], keratinocytes responded with increased mRNA expression levels for interleukin-6, tumor necrosis factor-α, interferon-β, RIG-I, IFI16, and MDA5. There was no detectable increase in mRNA expression, however, in keratinocytes infected with CPV-2. Furthermore, CPV-2-infected keratinocytes stimulated with poly(dA:dT) and poly(I:C) showed similar mRNA expression levels for these gene products when compared with expression levels in uninfected cells. These results suggest that although canine keratinocytes contain functional PRRs that can recognize and respond to dsDNA and dsRNA ligands, they do not appear to recognize or initiate a similar response to CPV-2.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.