37039344
An e-Learning Tool for the Austrian Citizen Card
Austria is one of the leading countries in the field of electronic identity. The Austrian Citizen Card System is presently going live; the next step is to inform the citizen about the functionalities and advantages, as well as on how to use the card. On authority of the Federal Chancellor’s Office, the Danube University has developed an e-learning tool that will be made available to all Austrian households. The target group is the citizen rather than the early technological adopter, and the aim is to explain the use of a citizen card rather than the technical background. The main pedagogical method therefore is to reduce technical complexity to an absolute minimum. This e-learning tool is positioned to be one piece of a sophisticated e-government communication strategy.
39402693
Re-inventing icons: using animation as cues in icons for the visually impaired
14362967
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks
Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises.
14525136
Model-based EEG and MEG Source Reconstruction Methods
Neurophysiological activity, as measured with EEG or MEG, has its origin in the brain. For most EEG and MEG experiments it is known beforehand, that the folded gray matter of the cortex is the location of the neurons whose activity is measured. These neurons, the pyramidal cells, are oriented perpendicular to the cortical surface. Triangulation of the segmented cortical surface yields a triangle net holding all anatomical information which is necessary to guide the reconstruction procedure. We have developed dedicated 3D region-growing, surface subsampling, normal estimation, and surface triangulation algorithms for generating the cortical triangle net. Especially, triangle nets encode neighborhood information of the potential source locations. Neighborhood information provides additional source constraints that model the correlation of neuronal activation which is necessary to generate detectable fields or potentials at all. In a surface-based 3D visualization environment, reconstruction results are displayed as color-coded overlays together with the cortical surface, the measurement setup, and arbitrary MR data slices. The algorithms described are part of the source reconstruction software package CURRY.
16477809
EventScope: Bringing Remote Experience of Mars to the Public through Telepresence
Telepresence, experiencing a place without physically being there, offers an important means for the public experience of remote locations such as distant continents or other planets. EventScope presents one such telepresence visualization interface for bringing scientific missions to the public. Currently, remote experience lessons based on NASA’s Mars Exploration Rover missions are being made available through the EventScope framework to museums, classrooms, and the public at large.
28503548
Early Outbreak Detection Using an Automated Data Feed of Test Orders from a Veterinary Diagnostic Laboratory
Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.
26946048
Exploiting Spatial and Temporal Locality of Accesses: A New Hardware-Based Monitoring Approach for DSM Systems
The performance of a parallel system with NUMA characteristics depends on the efficient use of local memory accesses. Programming and tool environments for such DSM systems should enable and exploit data locality.