Roadside Safety Library - Library Information Retrieval System (LIRS). Volume 2: programmers' and operators' manual. Final report.

Turner-Fairbank Highway Research Center, McLean, Va.

Local information retrieval for the simulation and modeling system /

COO-1469-0174.

Research study of criteria and procedures for evaluating scientific information retrieval systems; final report. Prepared for Office of Science Information Service, National Science Foundation, Washington, D.C., contract NSF-C218.

"AD 273 115."

FIRES : Fire Information Retrieval and Evaluation System : a program for fire danger rating analysis /

"August 1997."

Information retrieval and machine translation : based on the International Conference for Standards on a Common Language for Machine Searching and Translation /

Mode of access: Internet.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Three-dimensional capture, representation, and manipulation of Cuneiform tablets

This paper presents the digital imaging results of a collaborative research project working toward the generation of an on-line interactive digital image database of signs from ancient cuneiform tablets. An important aim of this project is the application of forensic analysis to the cuneiform symbols to identify scribal hands. Cuneiform tablets are amongst the earliest records of written communication, and could be considered as one of the original information technologies; an accessible, portable and robust medium for communication across distance and time. The earliest examples are up to 5,000 years old, and the writing technique remained in use for some 3,000 years. Unfortunately, only a small fraction of these tablets can be made available for display in museums and much important academic work has yet to be performed on the very large numbers of tablets to which there is necessarily restricted access. Our paper will describe the challenges encountered in the 2D image capture of a sample set of tablets held in the British Museum, explaining the motivation for attempting 3D imaging and the results of initial experiments scanning the smaller, more densely inscribed cuneiform tablets. We will also discuss the tractability of 3D digital capture, representation and manipulation, and investigate the requirements for scaleable data compression and transmission methods. Additional information can be found on the project website: www.cuneiform.net

Fast Information Retrieval in the Open Grid Service Architecture

This is an extended version of an article presented at the Second International Conference on Software, Services and Semantic Technologies, Sofia, Bulgaria, 11–12 September 2010.

Students’ Digital Archive at the State University of Library Studies and Information Technologies, Sofia

The paper describes the creation and content of the digital archive of photographs, films and materials from fieldwork (interviews, surveys, and observations) of students from the Information Funds of the Cultural and Historical Heritage program at the State University of Library Studies and Information Technologies, Sofia, Bulgaria. The text discusses the educational opportunities of the archive, and the plans for publishing it as CD and for conversion into an electronic archive on the Internet.

User-centric music information retrieval

The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for. Traditionally, songs were organized in a hierarchical structure such as genre->artist->album->track, to facilitate the users’ navigation. However, the intentions of the users are often hard to be captured in such a simply organized structure. The users may want to listen to music of a particular mood, style or topic; and/or any songs similar to some given music samples. This motivated us to work on user-centric music retrieval system to improve users’ satisfaction with the system. The traditional music information retrieval research was mainly concerned with classification, clustering, identification, and similarity search of acoustic data of music by way of feature extraction algorithms and machine learning techniques. More recently the music information retrieval research has focused on utilizing other types of data, such as lyrics, user-access patterns, and user-defined tags, and on targeting non-genre categories for classification, such as mood labels and styles. This dissertation focused on investigating and developing effective data mining techniques for (1) organizing and annotating music data with styles, moods and user-assigned tags; (2) performing effective analysis of music data with features from diverse information sources; and (3) recommending music songs to the users utilizing both content features and user access patterns.

Vocabulary and language model adaptation using information retrieval

International audience