November 28, 2006

International Conference on Asian Digital Libraries (ICADL2006) in Kyoto day 2

"A Digital Resource Harvesting Approach for Distributed Heterogeneous Repositories", Yang Zhao, Airon Jiang (Tsinghua University Library, China)

They present a digital resource harvesting system that uses a few different standards (which I don't really know about) to get around problems in using plain HTTP. Their messaging system is able to more easily transfer large data payloads. They use METS (metadata encoding and transmission standard) which is supported by some digital library federation. They have tested their system in CALIS-ETD, a large Chinese consortium for distributing electron thesis and dissertation.

"Parallelising Harvesting", Hussein Suleman (University of Cape Town, South Africa)

For two open source systems, EPrint and DSpace, they both become quite slow with over 100,000 documents. In pure sciences (Physics, Chemistry, etc.) they have lots of data and use high performance computer systems to handle the large sets of data. He explains some types of large computer systems (clusters, multi-core, grid, etc.) He used OpenMosix open source system to create a cluster.

For harvesting there is the standard OAI Protocol for Metadata harvesting transfers. Since that is a serial protocol, there is a token passed around the grid and only the token owning node can request data. The rest process data. He did some experiments on different architectures impacts on data transfer and data indexing. In the end, if you have more computation to do on the data than data transfer, the cluster is a win.

Also, the OAI protocol needs to be extended to allow for parallel access.

Session 5a: Personalization in Digital Libraries

"Personlized Information Delivering Service in Blog-like Digital Libraries", Jason J. Jung (Inha University, Korea; INRIA Rhone-Alpes, France.)

In the Blogosphere there is a problem of information overload, it is difficult to find relevant information. Another problem is the network isolation phenomenon. The network space is generally a personal set of links. The speaker's BlogGrid is a method to approach these problems. They define and collect user activities, extract user preferences, and then make recommendations to the users. The system follows users' posts, their linking patterns, their navigation patterns (Random or access to neighbors), their responses (trackbacks, comments), and blog categories (using ODP or TopicMap taxonomies.)

When users exhibit similar response patterns, we can assume that they exhibit the same interests. The speaker wants to find "virtual hub", so they use the distance between users, and hub weight which looks to be similar to the Google algorithm (since the equation has authorities in there too.)

They had a user evaluation where they asked students to track new information, some using BlogGrid, some not. Most liked the recommendations from the users, but some people did not like the system due to having to install it on their systems and privacy issues.

"A Personal Ontology Model for Library Recommendation System", I-En Liao (National Chung Hsing University, Taiwan), Shu-Chuan Liao (Asia University, Taiwan), Kuo-Fong Kao, Ine-Fei Harn (National Chung Hsing University)

There are two approaches: social filtering (other people who liked X also liked Y), and content-based filtering. This might not be as applicable to libraries because library users are not as interested in popularity; they have a specific information need. Their paper looks at content-based recommendation.

Their objectives are to automatically mine user interests from loan record, re-rank keywords based on a user interest score from a personal ontology. Their system is a web-based application. When the user logs in, their record is analyzed, then books are recommended to them. Their reference ontology could be Library of Congress (LCC) or a Chinese ontology (CCL). They build a personal ontology by using the loan record to find favorite categories from the reference ontologies. They copy over the nodes that have a value greater than some threshold. Then they try to find interesting keywords for the user. They compute a kind of TF*IDF over the keywords and then fetch books based on the value of their highest keywords (I'm surprised they didn't do some sort of weighting function, they just took the value of the highest rated keyword!)

"Research and Implementation of a Personalized Recommendation System", Li Dong (Tsinghua University, China), Yu Nie (SINA Corporation, China), Chunxiao Xing, Kehong Wang (Tsinghau University China).

They have a clustering algorithm (static that runs once, then dynamic for interacting with the user.) Their "cluster mining" approach allows data elements to overlap. (There are standard approaches in clustering that allow for that as well.) They did a clustering evaluation by looking at how many of the items in the cluster were recommended, but I don't know if they have a user evaluation included here. It looks like they also did some user evaluation that looks at how often the users took the recommendations, but I'm really not too sure about that. They used some data from a website where they are running their recommendation system.

Overall for the talks this morning, I feel like I should read the papers because in general the talks were not very good. Sometimes the presentation language (English) was a bit troublesome, other times I just didn't get a feel that the talk was well organized. Hussein Suleman's talk was nice, but he was aiming for more the librarian end of things, and was a bit simplified.

Session 7c: Multimedia Resource Retrieval and Organization

"Text Image Spotting Using Local Crowdedness and Hausdorff Distance", Hwa-Jeong Son, Sang-Cheol Park, Soo-Hyung Kim, Ji-Soo Kim, Guue Sang Lee, Deok Jai Choi (Chonnam National University, Korea)

They are looking at spotting text in an image given a query. They are taking the query as an image, instead of as text. They are trying to match the query image to a sub-region of the document image. They use the Hausdorff distance to match the images, and tried two other approaches: binary correlation and a modified Hausdorff distance.

Binary correlation just looks at an average of pixels in an overlapping region of the those images. You can move the image around to find the minimum distance over the entire map. (That seems quite computationally expensive!!) They also modify Hausdorff's approach to make it less computationally expensive by looking at "local crowdedness" - which looks to me like doing a blur operationand the taking points that are heavily dark from the bleed-over from adjacent pixels.

They build probability distributions for class given the features, and then classify based on the probability distributions given the features.

They test over 380 documents with 100%, 70%, 50%?, and 30%? of the query in the document -- but also half of those were used for training. So that is a very large document to query size I think. Their proposed method performed best.

For future research they are looking at scaling and font variations, and would like to look at newer faster features.

"Effective Image Retrieval for the M-learning System", EunJung Han, AnJin Park, DongWuk Kyoung (Soongsil University, Korea), HwangKyu Yang (Dongseo University, Korea), KeeChul Jung (Soongsil University Korea).

They are looking at blended learning environments where physical learning environments are augmented with virtual information on a PDA (camera to capture marks, screen to display information.) So they want to recognize real images instead of "pattern markers" and also to adapt their algorithms to the low-computational resources of PDAs.

They assume that the background is white, and the object to recognize is located in a central region. They propose some rotation and scale invariant features. They do boundary extraction, and then take the starting point as the closet pixel to the centroid of the object. That takes care of rotation. There is a problem when the closest boundary pixel is on a circle though, so they use the closest 3 pixels. They also use Dynamic Time Warping to compare images, which was much faster (a factor of 10) than an HMM approach.

Their approach can have problems when the shapes have similar boundaries, or when the boundaries are not extracted well. They had 87% recognition rate over 30 objects in their DB.

"Language Translation and Media Transformation in Cross-Language Image Retrieval", Hsin-Hsi Chen, Yih-Chen Chang (National Taiwan University, Taiwan) .

For cross-media information retrieval, queries and documents are in different media. In this paper they are dealing with cross-language image retrieval. In their format the images can be annotated in multiple languages. They build a transmedia dictionary by looking at tagged images, breaking it up into bits, and assigning visual features to the image tags.

They make a kind of comparable corpus by doing first content based retrieval, and then take the retrieved images and use their tags to build a text-based query. They present several methods for doing the translation at different times with different information (relevance feedback or not, include query translation or not, merging results in different ways, etc.) Adding the content information improves performance over just doing content-based IR.

"A Surface Errors Locator System for Ancient Chinese Culture Preservation", Yimin Yu, Duanqing Xu, Chun Chen, Yijun Yu, Lei Zhao (Zhejiang University, China).

I think this talk was cancelled because the speaker was not here.
The evening banquet is at The Garden Oriental Kyoto.


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