I am pretty sure that Nicolas posted already something about this. He was taking a lot lately on how to improve collaboration in these fourth places.
The You’re In Control system uses computation to enhance the act of urination. Sensors in the back of a urinal detect the position of impact of a stream of urine, enabling the user to play interactive games on a screen mounted above the urinal.
Tags: new technology, wierd
NLTK, the Natural Language Toolkit, is a suite of Python libraries and programs for symbolic and statistical natural language processing. NLTK includes graphical demonstrations and sample data. It is accompanied by extensive documentation, including tutorials that explain the underlying concepts behind the language processing tasks supported by the toolkit.
Fabien is making big progress on the STAMPS interface. He was able, with the help of Satyndra to pass the CellID information that is collected by a Symbian script to the J2ME midlet of STAMPS. For the moment, it appears in the upper left corner of the screen, coded as (Local Area Information)LAI:MCC:MNC:LAC. Then the Cell ID (CID). This trick works only on MIDP 2.0 devices.
I found this project extremely fascinating: how to visualize graphically conversation undergoing on public forums. Here Andrew Fiore et. al. decided to relate the area of a square to the number of messages in a particular forum. Suddenly the picture becomes a fractal showing the complexity of our world coming out from a simple set of instruction, in the same fashion of Wolfram’s New Kind of Science.
I can summarize the papers I have been reading lately in three categories:
1. Research around the SOM method (Self-Organising Maps): SOM and WEBSOM are efficient algorithms to find correspondence between textual documents and to map these similarities on a two-dimensional graphical representation.
2. Research around the context automatic recognition: studies of Flanagan, reports and details methodologies for aggregating sensory data into arrays and matrices for unassisted clustering.
3. Research around spatial data mining: a canadian research group directed by Han has detailed methodologies like CLARANS for clustering elements with no geographical features using their geographical position.
This last group, in fact, results particularly useful for my research. What I need at this point is to put together methodologies for semantic and geographic clustering in some ways. Is there a priority between these two? Can we define a mixed approach? Are these two techniques independent? How can I merge them?
Tags: clustering, context, spatial clustering
Jamie advised me to read this book, which I found extremely interesting for my research:
“Spatial Cluster Modelling” by Andrew Lawson and David Denison, CRCPress.
Here is a preview on Google Press.
Tags: spatial clustering
J. Himberg, J. A. Flanagan, and J. Mäntyjärvi. Towards context awareness using symbol clustering map. In Proceedings of the Workshop for Self-Organizing Maps 2003 (WSOM2003), pages 249–254, Kitakyushu, Japan, 2003. [url]
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This paper present the Symbol Clustering Map methods that is used to recognizing the context of use of a mobile phone. The authors present an approach for context recognition that is based in a vectorial fusion of sensory information. Two sorts of fusions are presented: a multi-source fusion and a temporal fusion of events. The Symbol Clustering Map (SCM) is based on a lattice structure, generally two-dimensional, with a total of M x M lattice points. For each node k of the lattice, a symbol set is associated, combined with a weight vector.
Tags: clustering, context
(via)
This is a web-based application charting the precise locations of closed-circuit television (CCTV) surveillance cameras in urban environments. this map tool enables users to plot exact routes that avoid these cameras, thus creating ‘paths of least surveillance’.
Is interesting how technology can help to overcome technology itself. Also, it is nice to see these kind of information displayed on a map.
Tags: maps, new technology
(via)
House_n is a Department of Architecture research group at the Massachusetts Institute of Technology that explores how new technologies, materials, and strategies for design can make possible dynamic, evolving places that respond to the complexities of life. Major House_n initiatives include The PlaceLab and the Open Source Building Alliance.
Lots of cool projects and ideas. One of the most common is that of placing sensors everywhere to monitor and generate a model of people’s activities in home environment. This can later be used for a possible support.
Copyright notice: the present content was taken from the following URL, the copyrights are reserved by the respective author/s.
Tags: context, new technology, wierd
K. Lagus, S. Kaski, and T. Kohonen. Mining massive document collections by the websom method. Information Sciences, 163(1-3):135–156, 2004. [url]
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This article present a summary of the research on the WEBSOM method, which is a viable salternative to the traditional text-mining methods. The idea behing the WEBSOM is to use a visual two-dimensional display to help browsing the document collections. In such maps, documents are displayed using representative keywords. The distance between these keywords is related to the similarity between the linked documents.
The authors present in the paper a review of the methods currently used for encoding the documents statistically in such a way that they can be represented using the WEBSOM. Some of them include: the vector space method, methods for dimensionality reduction, latent semantic indexing, random projection, word clustering, rapid initialization increasing map size, etc.
Additionally, the authors offer som other computational shortcut of the usage of their method: parallelized batch map algorithm, etc.
The paper contains also an evaluation of the new method with the traditional SOM algorithm, where they used two performance indices: the average quantization error and the classification accuracy.
Tags: clustering, spatial clustering
