Classifying Amharic News Text Using Self-Organizing Maps

The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field. The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

A Collaborative Lecture in Information Retrieval for Students at Universities in Germany and Switzerland

K3, work in progress, is an acronym for Kollaboration (collaboration), Kommunikation (communication), and Kompetenz (competence). K3 provides a platform in the context of knowledge management to support collaborative knowledge production in learning environments. The underlying hypothesis states that collaborative discourse conciliates information as well as communication competence in learning contexts. The collaborative, communicative paradigm of K3 is implemented by asynchronous communication tools as a means of constructivist learning methodology. In this paper we will describe a K3 course. The lecture was organized and carried out at two places in two different countries (Germany and Switzerland) with students from different universities in the context of Library and Information Science. The paper informs about the management of the lecture and about the problems we had to run the lecture at two places. The circumstances in coordinating the presentations, the exercises, the examinations and evaluation, and the time schedule are presented. The conclusions of the lecturers and the results of a questionnaire for the students are explained in detail