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An unsolved problem in medical image analysis is validation of methods. In this paper we will focus on image registration and in particular on nonlinear image registration, which is one of the hardest analysis problems to validate. The paper covers currently used methods of validation, comparative challenges and public datasets, as well as some of our own work in this area.
With the increasing size of digital libraries (DLs) it has become a challenge to identify author names correctly and assign publications to them. The situation becomes more critical when different persons share the same name (homonym problem) or when the names of authors are presented in several different ways (synonym problem). This paper focuses on homonym names in the computer science bibliography...
The purpose of this work is to compare temporal gait parameters from three different IMU locations to the gold standard force platform. 33 subjects (12 F, 21 M) performed twenty gait trials each while wearing inertial measurement units (IMUs) on the trunk, both shanks and both feet. Data was simultaneously collected from a laboratory embedded force plate. Step times were derived from the raw IMU data...
Scientific ranking has long been a hot and important topic in both computer science and scientometrics. A lot of statistics-based and graph-based methods have been proposed for calculating a prestige value as the assessment of each paper's scientific influence. However, being ignorant of the dynamic nature of scientific publication and science evolution, all these methods present a biased point of...
Literature-Based Discovery (LBD) is the science of relating existing knowledge in literature to discover new relationships. It is sometimes referred to as hidden knowledge. This paper provides the most recent classification of the existing LBD methods relating the problem to other domains such as information retrieval. The papers identifies that Vector Space Model, Probabilistic Model, and Inference...
A complete ontology does not contain all domain information yet may still meet the requirements. Evaluation of ontology is necessary to determine the quality. To evaluate the quality of an ontology, various criteria have been proposed. This paper is a tribute to the works done through comparative study with the goal of identifying overlaps and establishing relationship of the various criteria. A framework...
This paper reports initial research results related to the use of locality-sensitive hashing (LSH) for string-based matching of big ontologies. Two ways of transforming the matching problem into a LSH problem are proposed and experimental results are reported. The performed experiments show that using LSH for ontology matching could lead to a very fast matching process. The quality of the alignment...
Arabic Natural Language Processing (ANLP) has known an important development during the last decade. Nowadays, several ANLP tools are already developed such as morphological analyzers. These analyzers are often used in more advanced applications such as syntactic parsers, search engines, machine translation systems, etc. However, the choice of a morphological analyzer to use, among others, can be...
Clinical knowledge systems (CKS) facilitate healthcare practitioners to make the best decisions at the point of care by having access to most recent knowledge. However, because of the overabundance of clinical research articles, it is time consuming to add new content manually to any CKS such as UpToDate and to ensure that it is consistent with evidence. For this reason, many CKS such as Mayo Clinic's...
Web applications and server environments hosting them rely on configuration settings that influence their security, usability, and performance. Misconfiguration results in severe security vulnerabilities. Recent trends show that misconfiguration is among the top critical risks in web applications. While effective at uncovering numerous classes of vulnerabilities, generic web application vulnerability...
Detecting clones from large datasets is an interesting research topic for a number of reasons. However, building scalable clone detection tools is challenging and it is often impossible to use existing state of the art tools for such large datasets. In this research we have investigated the use of our Shuffling Framework for scaling classical clone detection tools to ultra large datasets. This framework...
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