Report on the Third ACM/IEEE Joint Conference on Digital Libraries (JCDL)

The Third ACM/IEEE Joint Conference on Digital Libraries (JCDL 2003) was held on the campus of Rice University in Houston, Texas, May 27 - 31. Regarding the merging of the ACM and IEEE conference series, in the JCDL 2002 conference report published last year in D-Lib Magazine Edie Rasmussen noted, Perhaps by next year...no one will remember that it wasn\u27t always so [1]. Judging by the number of participants I met who did not know that the ACM and IEEE used to hold separate digital library conferences, Rasmussen\u27s prediction has come to pass

Evolution of Decision Support Systems Research Field in Numbers

The scientific production in a certain field shows, in great extent, the research interests in that field. Decision Support Systems are a particular class of information systems which are gaining more popularity in various domains. In order to identify the evolution in time of the publications number, authors, subjects, publications in the Decision Support Systems (DSS) field, and therefore the scientific world interest for this field, in November 2010 there have been organized a series of queries on three major international scientific databases: ScienceDirect, IEEE Xplore Digital Library and ACM Digital Library. The results presented in this paper shows that, even the decision support systems research field started in 1960s, the interests for this type of systems grew exponentially with each year in the last decades.DSS, Numbers, Research, Materials

A Digital Library of Folklore Songs and Keyword-Based Search Engine

ACM Computing Classification System (1998): H3.3, H.5.5, J5.We present a full text search engine in a digital library of Bulgarian folklore songs - in the lyrics and scores of the songs. The deployment of the digital library on the cloud as well as the technical requirements for providing our data to Europeana are discussed.This work is partially supported by Grant of the Bulgarian National Science Foundation under number DTK-02-54/2009

Concurrent Bounded Model Checking

The Definitive Version can be found in the ACM Digital Library here: http://dx.doi.org/10.1145/2693208.2693240issue_date: January 2015 numpages: 5 acmid: 2693240 keywords: Bounded Model Checking, Concurrency, Symbolic Executionissue_date: January 2015 numpages: 5 acmid: 2693240 keywords: Bounded Model Checking, Concurrency, Symbolic Executionissue_date: January 2015 numpages: 5 acmid: 2693240 keywords: Bounded Model Checking, Concurrency, Symbolic Executio

Intelligent Fusion of Structural and Citation-Based Evidence for Text Classification

This paper investigates how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity, five derived from the citation structure of the collection, and three measures derived from the structural content, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM digital library and the ACM classification scheme show that we can discover similarity functions that work better than any evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers