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- Hasso-Plattner-Institut für Digital Engineering gGmbH (12) (remove)
3D point cloud technology facilitates the automated and highly detailed acquisition of real-world environments such as assets, sites, and countries. We present a web-based system for the interactive exploration and inspection of arbitrary large 3D point clouds. Our approach is able to render 3D point clouds with billions of points using spatial data structures and level-of-detail representations. Point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering, e.g., based on semantics. A set of interaction techniques allows users to collaboratively work with the data (e.g., measuring distances and annotating). Additional value is provided by the system’s ability to display additional, context-providing geodata alongside 3D point clouds and to integrate processing and analysis operations. We have evaluated the presented techniques and in case studies and with different data sets from aerial, mobile, and terrestrial acquisition with up to 120 billion points to show their practicality and feasibility.
Ubiquitous computing has proven its relevance and efficiency in improving the user experience across a myriad of situations. It is now the ineluctable solution to keep pace with the ever-changing environments in which current systems operate. Despite the achievements of ubiquitous computing, this discipline is still overlooked in business process management. This is surprising, since many of today’s challenges, in this domain, can be addressed by methods and techniques from ubiquitous computing, for instance user context and dynamic aspects of resource locations. This paper takes a first step to integrate methods and techniques from ubiquitous computing in business process management. To do so, we propose discovering commute patterns via process mining. Through our proposition, we can deduce the users’ significant locations, routes, travel times and travel modes. This information can be a stepping-stone toward helping the business process management community embrace the latest achievements in ubiquitous computing, mainly in location-based service. To corroborate our claims, a user study was conducted. The significant places, routes, travel modes and commuting times of our test subjects were inferred with high accuracies. All in all, ubiquitous computing can enrich the processes with new capabilities that go beyond what has been established in business process management so far.
From face to face
(2019)
Despite advances in the conceptualisation of facial mimicry, its role in the processing of social information is a matter of debate. In the present study, we investigated the relationship between mimicry and cognitive and emotional empathy. To assess mimicry, facial electromyography was recorded for 70 participants while they completed the Multifaceted Empathy Test, which presents complex context-embedded emotional expressions. As predicted, inter-individual differences in emotional and cognitive empathy were associated with the level of facial mimicry. For positive emotions, the intensity of the mimicry response scaled with the level of state emotional empathy. Mimicry was stronger for the emotional empathy task compared to the cognitive empathy task. The specific empathy condition could be successfully detected from facial muscle activity at the level of single individuals using machine learning techniques. These results support the view that mimicry occurs depending on the social context as a tool to affiliate and it is involved in cognitive as well as emotional empathy.
Kyub
(2019)
We present an interactive editing system for laser cutting called kyub. Kyub allows users to create models efficiently in 3D, which it then unfolds into the 2D plates laser cutters expect. Unlike earlier systems, such as FlatFitFab, kyub affords construction based on closed box structures, which allows users to turn very thin material, such as 4mm plywood, into objects capable of withstanding large forces, such as chairs users can actually sit on. To afford such sturdy construction, every kyub project begins with a simple finger-joint "boxel"-a structure we found to be capable of withstanding over 500kg of load. Users then extend their model by attaching additional boxels. Boxels merge automatically, resulting in larger, yet equally strong structures. While the concept of stacking boxels allows kyub to offer the strong affordance and ease of use of a voxel-based editor, boxels are not confined to a grid and readily combine with kuyb's various geometry deformation tools. In our technical evaluation, objects built with kyub withstood hundreds of kilograms of loads. In our user study, non-engineers rated the learnability of kyub 6.1/7.
We elaborate on the possibilities and needs to integrate design thinking into requirements engineering, drawing from our research and project experiences. We suggest three approaches for tailoring and integrating design thinking and requirements engineering with complementary synergies and point at open challenges for research and practice.
Selfish Network Creation focuses on modeling real world networks from a game-theoretic point of view. One of the classic models by Fabrikant et al. (2003) is the network creation game, where agents correspond to nodes in a network which buy incident edges for the price of alpha per edge to minimize their total distance to all other nodes. The model is well-studied but still has intriguing open problems. The most famous conjectures state that the price of anarchy is constant for all alpha and that for alpha >= n all equilibrium networks are trees. We introduce a novel technique for analyzing stable networks for high edge-price alpha and employ it to improve on the best known bound for the latter conjecture. In particular we show that for alpha > 4n - 13 all equilibrium networks must be trees, which implies a constant price of anarchy for this range of alpha. Moreover, we also improve the constant upper bound on the price of anarchy for equilibrium trees.
Duplicate detection algorithms produce clusters of database records, each cluster representing a single real-world entity. As most of these algorithms use pairwise comparisons, the resulting (transitive) clusters can be inconsistent: Not all records within a cluster are sufficiently similar to be classified as duplicate. Thus, one of many subsequent clustering algorithms can further improve the result. <br /> We explain in detail, compare, and evaluate many of these algorithms and introduce three new clustering algorithms in the specific context of duplicate detection. Two of our three new algorithms use the structure of the input graph to create consistent clusters. Our third algorithm, and many other clustering algorithms, focus on the edge weights, instead. For evaluation, in contrast to related work, we experiment on true real-world datasets, and in addition examine in great detail various pair-selection strategies used in practice. While no overall winner emerges, we are able to identify best approaches for different situations. In scenarios with larger clusters, our proposed algorithm, Extended Maximum Clique Clustering (EMCC), and Markov Clustering show the best results. EMCC especially outperforms Markov Clustering regarding the precision of the results and additionally has the advantage that it can also be used in scenarios where edge weights are not available.
In this paper, we establish the underlying foundations of mechanisms that are composed of cell structures-known as metamaterial mechanisms. Such metamaterial mechanisms were previously shown to implement complete mechanisms in the cell structure of a 3D printed material, without the need for assembly. However, their design is highly challenging. A mechanism consists of many cells that are interconnected and impose constraints on each other. This leads to unobvious and non-linear behavior of the mechanism, which impedes user design. In this work, we investigate the underlying topological constraints of such cell structures and their influence on the resulting mechanism. Based on these findings, we contribute a computational design tool that automatically creates a metamaterial mechanism from user-defined motion paths. This tool is only feasible because our novel abstract representation of the global constraints highly reduces the search space of possible cell arrangements.
Using Hidden Markov Models for the accurate linguistic analysis of process model activity labels
(2019)
Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.
Wendepunkt für Gesundheit
(2019)