On optical data-guided optimal control simulations of human motion

Multibody System Dynamics - This work addresses the synergistic fusion of optimal control simulations and marker-based optical measurements of human motion. The latter is a widespread capturing...     

Regulating self-organized collaborative learning: the importance of homogeneous problem perception,immediacy and intensity of strategy use

Very often, university students deliberately form self-organized study groups, e.g. to study collaboratively for an upcoming exam. Yet, very little is known about what regulation problems such self-organized study groups encounter during their learning process and how they try to cope with these problems. Therefore, this study investigates how completely self-organized groups (i.e., non-guided groups outside the classroom that form without external impulse) regulate their collaborative learning process when faced with different kinds of regulation problems. More specifically, we tested the hypotheses that members of self-organized study groups are more satisfied with their group learning experience (a) the more homogeneous their problem perceptions are within their group, (b) the more they apply immediate (rather than non-immediate) strategies to remedy their regulation problems, and (c) the more frequently they apply regulation strategies. In a longitudinal study, N?=?122 students, voluntarily studying for their exams in N?=?52 groups, were asked to indicate the types of problems they experienced, the types of strategies they used to tackle those problems, and their satisfaction with their group learning experience after each of their self-organized study meetings. Hierarchical linear modeling confirmed all hypotheses. Qualitative analysis of two selected groups’ self-reported situational data provided additional insights about the mechanisms that may have contributed to the results. Our study provides important directions for future research, including the recommendation to identify the processes by which groups (a) can reach homogeneity of problem perceptions and (b) coordinate the choice of appropriate strategies within the group.

     

PointProNets: Consolidation of Point Clouds with Convolutional Neural Networks

With the widespread use of 3D acquisition devices, there is an increasing need of consolidating captured noisy and sparse point cloud data for accurate representation of the underlying structures. There are numerous algorithms that rely on a variety of assumptions such as local smoothness to tackle this ill‐posed problem. However, such priors lead to loss of important features and geometric detail. Instead, we propose a novel data‐driven approach for point cloud consolidation via a convolutional neural network based technique. Our method takes a sparse and noisy point cloud as input, and produces a dense point cloud accurately representing the underlying surface by resolving ambiguities in geometry. The resulting point set can then be used to reconstruct accurate manifold surfaces and estimate surface properties. To achieve this, we propose a generative neural network architecture that can input and output point clouds, unlocking a powerful set of tools from the deep learning literature. We use this architecture to apply convolutional neural networks to local patches of geometry for high quality and efficient point cloud consolidation. This results in significantly more accurate surfaces, as we illustrate with a diversity of examples and comparisons to the state‐of‐the‐art.     

Towards User‐Centered Active Learning Algorithms

The labeling of data sets is a time‐consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual‐interactive labeling (VIAL) provides users an active role in the process of labeling, with the goal to combine the potentials of humans and machines to make labeling more efficient. Recent experiments showed that users apply different strategies when selecting instances for labeling with visual‐interactive interfaces. In this paper, we contribute a systematic quantitative analysis of such user strategies. We identify computational building blocks of user strategies, formalize them, and investigate their potentials for different machine learning tasks in systematic experiments. The core insights of our experiments are as follows. First, we identified that particular user strategies can be used to considerably mitigate the bootstrap (cold start) problem in early labeling phases. Second, we observed that they have the potential to outperform existing active learning strategies in later phases. Third, we analyzed the identified core building blocks, which can serve as the basis for novel selection strategies. Overall, we observed that data‐based user strategies (clusters, dense areas) work considerably well in early phases, while model‐based user strategies (e.g., class separation) perform better during later phases. The insights gained from this work can be applied to develop novel active learning approaches as well as to better guide users in visual interactive labeling.     

Semantic Segmentation for Line Drawing Vectorization Using Neural Networks

In this work, we present a method to vectorize raster images of line art. Inverting the rasterization procedure is inherently ill‐conditioned, as there exist many possible vector images that could yield the same raster image. However, not all of these vector images are equally useful to the user, especially if performing further edits is desired. We therefore define the problem of computing an instance segmentation of the most likely set of paths that could have created the raster image. Once the segmentation is computed, we use existing vectorization approaches to vectorize each path, and then combine all paths into the final output vector image. To determine which set of paths is most likely, we train a pair of neural networks to provide semantic clues that help resolve ambiguities at intersection and overlap regions. These predictions are made considering the full context of the image, and are then globally combined by solving a Markov Random Field (MRF). We demonstrate the flexibility of our method by generating results on character datasets, a synthetic random line dataset, and a dataset composed of human drawn sketches. For all cases, our system accurately recovers paths that adhere to the semantics of the drawings.     

NDNetGaming - development of a no-reference deep CNN for gaming video quality prediction

Multimedia Tools and Applications - Gaming video streaming services are growing rapidly due to new services such as passive video streaming of gaming content, e.g. Twitch.tv, as well as cloud...     

The Fractional CIO in SMEs: conceptualization and research agenda

We conceptualize the new phenomenon of the Fractional Chief Information Officer (CIO) as a part-time executive who usually works for more than one primarily small- to medium-sized enterprise (SME) and develop promising avenues for future research on Fractional CIOs. We conduct an empirical study by drawing on semi-structured interviews with 40 individuals from 10 different countries who occupy a Fractional CIO role. We derive a definition for the Fractional CIO, distinguish it from other forms of employment, and compare it with existing research on CIO roles. Further, we find four salient engagement types of Fractional CIOs offering value for SMEs in various situations: Strategic IT management, Restructuring, Rapid scaling, and Hands-on support. The results reveal similarities with existing CIO roles as well as novel insights concerning the different engagement types. Lastly, we propose a research agenda for the Fractional CIO field, based on four research themes derived from existing CIO research and insights from the interviews.

     

Visual Analysis of Confocal Raman Spectroscopy Data using Cascaded Transfer Function Design

2D Confocal Raman Microscopy (CRM) data consist of high dimensional per‐pixel spectral data of 1000 bands and allows for complex spectral and spatial‐spectral analysis tasks, i.e., in material discrimination, material thickness, and spatial material distributions. Currently, simple integral methods are commonly applied as visual analysis solutions to CRM data which exhibit restricted discrimination power in various regards. In this paper we present a novel approach for the visual analysis of 2D multispectral CRM data using multi‐variate visualization techniques. Due to the large amount of data and the demand of an explorative approach without a‐priori restriction, our system allows for arbitrary interactive (de)selection of varaibles w/o limitation and an unrestricted online definition/construction of new, combined properties. Our approach integrates CRM specific quantitative measures and handles material‐related features for mixed materials in a quantitative manner. Technically, we realize the online definition/construction of new, combined properties as semi‐automatic, cascaded, 1D and 2D multidimensional transfer functions (MD‐TFs). By interactively incorporating new (raw or derived) properties, the dimensionality of the MD‐TF space grows during the exploration procedure and is virtually unlimited. The final visualization is achieved by an enhanced color mixing step which improves saturation and contrast.     

A GPU‐Adapted Structure for Unstructured Grids

A key advantage of working with structured grids (e.g., images) is the ability to directly tap into the powerful machinery of linear algebra. This is not much so for unstructured grids where intermediate bookkeeping data structures stand in the way. On modern high performance computing hardware, the conventional wisdom behind these intermediate structures is further challenged by costly memory access, and more importantly by prohibitive memory resources on environments such as graphics hardware. In this paper, we bypass this problem by introducing a sparse matrix representation for unstructured grids which not only reduces the memory storage requirements but also cuts down on the bulk of data movement from global storage to the compute units. In order to take full advantage of the proposed representation, we augment ordinary matrix multiplication by means of action maps, local maps which encode the desired interaction between grid vertices. In this way, geometric computations and topological modifications translate into concise linear algebra operations. In our algorithmic formulation, we capitalize on the nature of sparse matrix‐vector multiplication which allows avoiding explicit transpose computation and storage. Furthermore, we develop an efficient vectorization to the demanding assembly process of standard graph and finite element matrices.