APOSDLE – New ways to work, learn and communicate
Contact: Stefanie Lindstaedt, slind[at]know-center.at
Abstract:
How does APOSDLE support businesses and their employees to continuously learn and master new knowledge? This movie follows Sandra, who is a CAD manager in an engineering company, through a typical workday. Find out how APOSDLE supports Sandra to continuously learn at work, to learn collaboratively in her work environment, and from resources available in her organisation.
- Download High Definition MPG-Video in English
- Download High Definition MPG-Video in German
- PAL DVD compressed as ZIP-File
We recommend using the VLC-Player for watching the movie: http://www.videolan.org/vlc/.
Domain Modeling Tool
Contact: Viktoria Pammer, vpammer[at]know-center.at
Abstract: (More details are currently only available in German)
Erstellen Sie basierend auf Ihren Dokumenten eine Ontologie, und annotieren Sie Ihre Dokumente mit Konzepten aus einer Ontologie. Das Domain Modeling Tool unterstützt Sie durch Methoden wie Termextraktion, Dokument-Clustering und automatische Dokument-Klassifikation.
eLearning Check
Abstract: (More details are currently only available in German)
Sind Sie bereit für eLearning? Diese Checkliste ist als Teil eines Forschungsprojekts entstanden, das im Jahr 2004 vom Know-Center Graz in Kooperation mit seinen Industriepartnern durchgeführt wurde. Die Checkliste soll Unternehmen dabei helfen zu erkunden, wie eLearning im Rahmen ihrer betrieblichen Weiterbildung Nutzen stiften kann und welche Voraussetzungen und Rahmenbedingungen dafür gegeben sein müssen. Die Checkliste hilft somit, Chancen und Risiken einer Umsetzung aufzudecken.
Fuzzy K-Means and K-Means Clustering Demo
Abstract:
Two demos showing K-Means Clustering, HAC and Fuzzy K-Means Clustering in 1D and 2D. The demos are written in Java and source is included. Cluster Demos This page contains 2 clustering demos:
Simply follow the links to see a screenshot with a short explanation for each of the 2 demos. For running the demos it is required that you have installed Java Web Start on your computer.
K-Means Demo
This simple application demonstrates the k-means algorithm. It can be very useful for generating nice illustrations as you can create test scenarios using drag&drop and repeat the clustering process with different starting positions for the cluster centroids. There are basically two modes of operation. In the first mode, you initialize the system with data, the application creates clusters of data which are generated using a normal (gaussian) distribution (you can select the standard deviation). In the second mode of operation, the k-means algorithm performs step-by-step the association of each data point to an appropriate cluster and displays the result. Additionally a voronoi diagram is drawn to have a better clue about the cluster seperations. Instructions:
- Adjust the sliders to fit your needs (Number of node per cluster, Number of clusters)
- Press the init button, the application generates M clusters with each having N nodes (using the slider values)Now you can move the clusters around by dragging the red boxesYou can also add new cluster by just clicking into the view canvas
- After you are satisfied with your test setup, press the start buttonNow the points are fixed and the initial centroids are drawnThe centroids can also be moved with simple drag&drop
- When you have finished to setup the initial centroid position you can press either step or runWhen pressing step, the algorithm performs one single stepPressing run has the effect, that the application runs until the algorithm converges (max. 20 iterations)
- Pressing reset causes the test setup to be reset to the state before pressing start
Download and Start Application here.
Fuzzy K-Means Demo
This application demonstrates the fuzzy k-means algorithm with a 1-dimensional test setup. Like the k-means demo, you can change the test setup using drag&drop for both, data points and cluster centroids. Also the modes of operation stay the same as before. First initialize the demo with data, change the data to fit your needs, and finally let the algorithm do the work. Additionally to drawing the data points and cluster centroids, also the membership function is drawn, to get a clue how it has an effect of clustering results. Instructions:
- Adjust the sliders to fit your needs (Number of points, Number of clusters)
- Press the init button, the application generates N points and M centroids (using the slider values)Now you can move the data points around by dragging them,the centroids can also be moved with simple drag&drop
- After you are satisfied with your test setup, press the start buttonNow the points are fixed and the initial membership function is drawn
- When you have finished to setup the initial centroid position you can press either step or runWhen pressing step, the algorithm performs one single stepPressing run has the effect, that the application runs until the algorithm converges (max. 20 iterations),max membership change is used as convergence criteria, and is checked against your accuracy setting.
- Pressing reset causes the test setup to be reset to the state before pressing start.
Download and Start Application here .
HAC Demo
This application demonstrates the hac algorithm with a 2-dimensional test setup. It is quite similar to the other 2 demo applications as you can tune your test setup using the appropriate slider (number of nodes) and simply drag&drop the points. Additionally there is a second view integrated, it will display the dendrogram of the clusters detected by the hac algorithm. There are 3 different algorithms implemented, Single-Link, Complete-Link and Average-Link. When the algorithm is working (e.g. in step mode) it is also possible to measure distances between the data points. Simply move your mouse over a data point, and depending on the selected linkage method the distance to the other clusters is calculated and displayed. Instructions:
- Adjust the slider to fit your needs (Number of points)
- Press the init button, the application generates N pointsNow you can move the data points around by dragging them,you can also add additionally data points by just clicking into the view
- After you are satisfied with your test setup, press the start buttonNow the points are fixed and the initial dendrogram is drawn
- Now you can press either step or runWhen pressing step, the algorithm performs one single stepPressing run has the effect, that the application runs until only one top cluster is left
- While the algorithm is running, you can measure distances between the several clustersby simply moving the mouse over the data points.
- Pressing reset causes the test setup to be reset to the state before pressing start
Download and Start Application here. Download Sources: Clusterdemos.src 57,53 kB
InfoSky Demo
Abstract:
Download the InfoSky Demo for visualising the DMOZ computers hierarchy with over 100.000 documents and 10.000 classes. This demo is based on a file system variant of InfoSky, also allowing to build and visualise own hierarchies. Demo Version (Windows Installer): here (~80 mb) Video: InfoSkyVideo.exe
iScan
Abstract: (More details are currently only available in German)
Ermitteln Sie in nur 10 Minuten die Innovationskraft Ihres Unternehmens und vergleichen Sie sich mit den Besten.
Link: http://www.iscan.at
MATURE
Contact: Barbara Kump, bkump[at]know-center.at
Abstract:
MATURE is a large-scale integrating project (IP), co-funded by the European Commission, Unit for Technology-Enhanced Learning (TEL) within Call 1 of the Seventh Framework Programme (FP7). It runs from April 2008 to March 2012.
MATURE brings together an experienced multi-disciplinary team of outstanding experts. To leverage their combined skills, it utilizes a participatory design methodology, involving companies inside and outside the consortium. MATURE has also begun to set up an associate partner network to maximize the project impact.
Mature Screencast: MATURE_D1_tagging_short
More information about the project: MATURE Website.
Neurovation
Abstract: (More details are currently only available in German)
Der Film stellt das Projekt Neurovation vor, in dem eine informationstechnisch unterstützte Kreativitätsmethode zum Einsatz am Arbeitsplatz entwickelt wird.
Neurovation_Film 10,1 MB
Publikation zum Projekt: Reinhard Willfort, Klaus Tochtermann, Aljoscha Neubauer (Hrsg.)” Creativity@Work für Wissensarbeit”
(ISBN: 978-3-8322-6028-6)
Sasu (Tag Recommender für Bilder)
Contact: Viktoria Pammer, vpammer[at]know-center.at
Abstract:
Imagine uploading a picture to Flickr, and getting automatic recommendations for tas. Our research prototype tagr recommends tags based on existing tags (if tags exist already), on the user, as well as on image classification and similar images. The classifier was trained on pictures in the Flickr-Group fruit & veg.
Demo-Video (Shockwave Player is required, and can be downloaded here)
Lindstaedt Stefanie, Pammer Viktoria, Mörzinger Roland, Kern Roman, Mülner Helmut and Wagner Claudia Recommending tags for pictures based on text, visual content and user context
Lindstaedt Stefanie, Pammer Viktoria, Mörzinger Roland, Kern Roman, Mülner Helmut and Wagner Claudia Recommending tags for pictures based on text, visual content and user context
