Refine
Year of publication
- 2015 (31) (remove)
Document Type
- Conference Proceeding (22)
- Article (3)
- Part of a Book (3)
- Contribution to a Periodical (2)
- Part of Periodical (1)
Language
- English (31) (remove)
Is part of the Bibliography
- no (31)
Keywords
- Data visualization (1)
- Framework (1)
- Image color analysis (1)
- Intercultural sharing (1)
- Knowledge sharing (1)
- Layout (1)
- National culture (1)
- Three-dimensional displays (1)
- Visualization (1)
Recently, rescue worker resources have not been sufficient to meet the regular response time during large-scale catastrophic events in every case. However, many volunteers supported official forces in different disaster situations, often self-organized through social media. In this paper, a system will be introduced which allows the coordination of trained volunteers by a professional control center with the objective of a more efficient distribution of human resources and technical equipment. Volunteers are contacted via app on their private smartphone. The design of this app is based on user requirements gathered in focus group discussions. The feedback of the potential users includes privacy aspects, low energy consumption, and mechanisms for long-term motivation and training. The authors present the results of the focus group analyses as well as the transfer to their app design concept.
With the spread of mobile devices among both, men and women, app-based games also become more popular. While traditionally, digital games are more famous among men, women seem to spend more time and money on mobile gaming. There are a lot of open questions with regard to women and gaming in general; research on gender differences in app-based mobile gaming is almost nonexistent. Taking an exploratory perspective, our study investigates gender differences in general usage patterns, attachment towards the game and motivational differences for choosing to play the famous QuizClash app. Also, we identify differences in reported and actual performance in specific categories and capture anticipation of success as well as likeliness of choosing specific knowledge categories depending on the opponents’ performance profile.
In this demo paper we present a new visualization technique for dynamic networks. It displays the time slices of the dynamic network using two dimensional graph layouting algorithms and stacks these in the third dimension to show the development over time. The visualization ensures that the same node always has the same position in each time slice so that it is easy to follow its development. It also allows filtering data and influencing node appearance based on properties. Additionally we offer a two dimensional comparison view for two time slices which highlights changes in graph structure and (if available) in measures of nodes. The presented visualization technique is implemented using Web technology and is available in a Web-based analytics workbench. We demonstrate the benefits of these techniques by an analysis of a data set from a learning community.
We present a novel approach of distributing matrix multiplications among GPU-equipped nodes in a cluster system. In this context we discuss the induced challenges and possible solutions. Additionally we state an algorithm which outperforms optimized GPU BLAS libraries for small matrices. Furthermore we provide a novel theoretical model for distributing algorithms within homogeneous computation systems with multiple hierarchies. In the context of this model we develop an algorithm which can find the optimal distribution parameters for each involved subalgorithm. We provide a detailed analysis of the algorithms space and time complexities and justify its use with a structured evaluation within a small GPU-equipped Beowulf cluster.
In the field of magnetic inductance tomography,
signal processing is a real challenge. This is due to the divergent
nature of magnetic fields. The sensitivity, i.e. the change in the
receiving signal by means of an electrically conductive sample
in a measuring volume depends strongly on the positioning
of the sample. Objects that are located near the transmitting
or receiving coils are very well locatable, where objects in
larger distance are hard to detect. In this paper an approach
is presented that improves the topology of the magnetic fields
in the ”magnetic induction tomography” (MIT) by changing
geometric constructions and current patterns of coils so far,
as to allow a sharper localization of objects within the space.
The aim is to level the distribution of the sensitivity in the
measuring volume, so that electrically conductive objects with
a larger distance between transmitting and receiving unit can
be detected with almost the same signal intensity as objects
close to the transmitting and receiving unit. The simulation tool
Comsolic is used for the geometric modeling making a finite
element analysis (FEA). The subsequent signal processing and
analysis of the simulation results are implemented in Matlabic .
Within this FEA the coil geometries and current patterns are
changed numerically, so that the minimum object size, that is
still detectable, is, compared to the known MIT, reduced and the
sensitivity of the system is improved. To validate the simulation in
Comsolic , first simulation results are compared with analytical
models and analyses.
Global software development changes the requirements in terms of soft competency and increases the complexity of social interaction by including intercultural aspects. While soft competency is often seen as crucial for the success of global software development projects, the concrete competence requirements remain unknown. Internationalization competency represents one of the first attempts to structure and describe the soft competence requirements for global software developers. Based on the diversity of tasks, competence requirements will differ among the various phases of software development. By conducting a survey on the importance of internationalization competences for the different phases of global software development, we identified differences in terms of competence importance and requirements in the phases. “Adaptability” (of one's working style) and “Cultural Awareness” were the main differences. “Cultural Awareness” distinguishes requirements engineering and software design from testing and implementation while “Adaptability” distinguishes implementation and software design from requirements engineering and testing.
This contribution demonstrates the efficient embedding of a single depth-camera into the automotive environment making mid-air gesture interaction for mobile applications viable in such a scenario. In this setting a new human-machine interface is implemented to give an idea of future improvements in automation processes in industrial applications. Our system is based on a data-driven approach by learning hand poses as well as gestures from a large database in order to apply them on mobile devices. We register any movement in a nearby driver area and crop data efficiently with the means of PCA transforming it into so-called feature vectors which present the input for our multi-layer perceptrons (MLPs). After MLP classification, the interpretation of user input is sent via WiFi to a tablet PC mounted into the car interior visualizing an infotainment system which the user is able to interact with. We demonstrate that by this setup hand gestures as well as hand poses are easily and efficiently interpretable insofar as that they become an intuitive and supplementary means of interaction for automotive HMI in mobile scenarios realizable in real-time.
We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.