There are a couple of scenarios that I have been developing to increase the usage of ShoutSpace at EPFL. I am in the need of Data so, every application that could bring me messages is appealing to me.

Scenario 1: Maintenance Group at EPFL. The idea is to give SS to the maintenance group so that they can use it as a support to coordinate their intervention on the site. Ideal would be that they could use it consistently over a certain period of time to optimize the working schedule. A Typical usage can include the items to be repaired and those that have been repaired already. Also they could annotate rooms that need to be refurnished. Exhibition panels that need to be moved and where. Here the mix of annotation and place and coordination takes its full power.

Scenario 2: Information Kiosk at EPFL. The idea is to translate SS into a Java Applet that can be executed from the information points at EPFL. There people could log-into the system and browse the campus looking for specific localized informations. For instance, where is a cafeteria near by. Additionally they can leave notes to coordinate for meetings on campus. People uses the kiosks to look for phone numbers or information about courses. In the same way they can use SS to look for specific information about features of places. Or to read the annotations left by other users.

Scenario 3: Newsgroup of EPFL into ShoutSpace. The idea is to import the EPFL newsgroup into ShoutSpace so that they can be mapped to the point of emittance or reception. Some specific newsgroup like Petit-Announces are extremely geographical in which they target specific points of interests (i.e., the room of the poster in case of selling/buing message or the zone interested by a particular maintenance). Offering these messages with a map support seems to be a great add-on. Additionally it would increase the value of using SS in daily routines.

Scenario 4: ShoutSpace as a support for event organization. Periodically events are organized at EPFL for the students life (i.e.,  le Balelec). A great deal of spatial management is needed to coordinate these efforts of moving materials, coordinating security, etc. The ideal would be for the organizers to use SS as a support tool for the organization of the event.

Scenario 5: ShoutSpace as support for an urban planning class of ENAC. The architecture faculty of EPFL has courses of Urban Planning. One possible class work could be the study of the EPFL site as a micro-city environment, defining the critical aspect of the site that could be redesigned. The class could use SS for the semester, leaving messages and annotations connected with their observation period. Subsequently the notes could be reported in the classroom for teaching and discussion purposes.

 

1- What is the current research on SA? Is it used? Why it did not have the some success of others techniques?

2- What are the advantages of using SA over SVD (Scalar Vector Decomposition)?

3- At the methodological level how is it possible to evaluate the performance of a IR system over another, given the fact that some of the parameters used for these measures are human based (i.e., defining what is relevant in a given collection returned by a query)?

Please feel free to chime in!

Tags: information retrieval, spreading activation

Recall is the proportion of all documents in the collection that are relevant to a query and that are actually retrieved.

Precision is the proportion of the retrieved set of documents that is relevant to the query.

The fallout rate is the ratio between number of non relevant documents retrieved and the total number of non relevant documents in the collection.

Tags: information retrieval

F. Crestani. Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11(6):453–482, December 1997. [pdf]

————–

This paper contains a literature review on the theme of spreading activation techniques used in information retrieval, which is a prticular instance of associative retrieval. The paper offers, at first, an overview of previeus research on this theme focussing on the associative retrieval with spreading activation.

One of the most common technique to build the associative network used by this technique is to expanding the original query with term-term, term-document and document-document associations. This technique is based on the assumption that there are statistically determinable relations among the tems, and between the terms and the documents. This assumption is also accounted for some of the drawback of the methods, because it is questioned the general applicability of the similarity measures derived statistically in domains which are extern to the knowledge domain. Additionally these similarities are computed under the assumption that the terms in the documents are originally uncorrelated.

The rest of the paper shows a couple of applications using Spreading Activation for IR. Some of the result shows a lack of consisten improvement of the effectivenes of the retrieval over other methods (i.e., vector processing model). Additionally, one of the drawback of SA is the difficulty of building and maintaining the network of connections between the elements (i.e., documents and terms).

One interesting application is that of Kimoto and Iwadera, AIRS, which propose to use an SA technique in connection with a dynamic thesaurus, which is a network where nodes represent terms and links represent semantic relationship between terms.

Another interesting idea proposed in the paper is that of using automatic constructed hypertext to facilitate the IR with the SA technique. 

Tags: information retrieval, Latent Semantic Analysis, machine learning, Singular Value Decomposition, text data mining

I found these three degree of good formatting for visual presentation:

[1] Legibility – distinctness that makes perception easy

[2] Readability – writing (print or handwriting) that can be easily read

[3] Clarity – free from obscurity and easy to understand; the comprehensibility of clear expression

Therefore the first level belong to the perception, the send to the presentation and the third to the content.

GeoSpace provides users with two types of interaction. First, the user can use text or speech to enter a query to which the system responds using the mechanism described above. Second, the user can use the mouse to zoom, pan, and move around the two and three dimensional map display.

First, the user asks the system “Show me Cambridge.” Then, the label Cambridge increases in opacity to bring this information to the user’s attention. The typographic size also changes accordingly, resulting in a sharper focus of Cambridge. Notice also that related information such as hospitals, highways and colleges around Cambridge are also identified visually to a slightly lesser degree. This demonstrates the reactive nature of discerning information rapidly in a visually dense environment.

Tags: information retrieval, machine learning, maps

I. Lokuge and S. Ishizaki. Geospace: An interactive visualization system for exploring complex information spaces. In I. R. Katz, R. L. Mack, L. Marks, M. B. Rosson, and J. Nielsen, editors, CHI95: Human Factors in Computing Systems, CHI 95 Conference Proceedings, pages 409–414, Denver, Colorado, USA, May 7-11 1995. ACM, Addison-Wesley. [pdf]

—————————–

This  paper present a seminal attempt to use the Spreading Activation Theory for information retrieval in multi-model user interfaces. The main reason for them to use this technique is that it follows the natural learning path of the user, which find it difficult to formulate his/her information seeking goal. Additionally, the advantage is that the ASN (activation spreading network) preserve’s the user previous state of exploration.

The system was tested with an interface called GeoSpace, that allowed the user to formulate vocal query on spatial features of the Boston’s area. The interface was conceived as a series of chained layers containing the relevant information. Each layer’s opaquity was regulated by the spreading retrieval engine.

Additionally, the author were trying to incorporate a simple learning mechanism that could associate different layers based on the retrieval’s order as suggested by the user.

3-4-2007 -> Geospace was commented by Frohlich [1997] as a dynamic homefinder system you can talk to, since it combines natural language input with a map formalism output to five users access to a database of area and property information.

This form of mixed mode interaction shows how the characted of model workd or conversational interfaces can be altered by introducing elements of the complementary mode into the input or output interface.

Tags: graphical user interface, human computer interaction, information retrieval, Location Based Services, machine learning, map algorithms, maps

Nuvvo 2.0, is a service for Web-based interactive learning is now free. Instructors and students both can use Nuvvo to teach and learn free of charge, improving accessibility to education and encouraging the free exchange of knowledge and ideas. The on-demand learning management system (LMS) allows individual instructors to teach online with included content creation, blogging, multimedia integration, evaluation tools, and portal customization tools. Instructors sign up and receive their own online learning portal from which to enroll students and run unlimited courses.

Tags: collaboration tools, education, learning technology, new technology, pedagogy

I am excited as a computer scientist can be … I think. Today I completed a draft version of the STAMPS query tester. This little application will be of a tremendous use to compare different kinds of retrieval techniques on the STAMPS dataset.

What I like of this interface is that it keep things essential, as I like them. However it still show lots of complexity in the output. I realized this using PythonCard a wxPython wrapper.

Tags: graphical user interface, information retrieval, Python

Prompted by a comment on this previous post, I was looking for some bibliographies on MobileLearning, which I found here: [1], [2], [3], and [4]. However, this collections are not organized at all. One of the best reviewed collection of articles is the one by Future Lab.

Additionally, for the current (future?) trends, here is the summary of the WMTE conference by the ‘never sleeping’ Nicolas.

Tags: mobile learning