Customization of digital library of PhD dissertations for citizens

PHD UNS is digital library of PhD dissertations defended at University of Novi Sad. A web page for basic and advanced search has been developed in order to improve discoverability of dissertations stored in the digital library. This paper presents customization of PHD UNS web search pages for citizens out of academy. The customization includes extension of available representation styles and implementation of automatic recommendations of PhD dissertations. Representation styles are extended with textual representation specially designed for non-academic citizens and visual representation based on word clouds. Automatic recommendations are based on collaborative approach built on PhD download history, i.e., performed on the basis of what other ‘similar’ users have found useful. The PHD UNS digital library logs information for each dissertation downloading. Besides basic information about downloaded dissertation, those logs also contain information about client machine which requested downloading. Those logs have been used in order to prove our customization really improve non-academic users’ experience

From Idea to Functional ETD: Experiences from the University of Novi Sad, Serbia

This paper reviews different phases of introducing and usage of Electronic Theses and Dissertations – ETD at the University of Novi Sad with special emphasis on specific requirements, challenges and further directions of development and use of ETD systems at the University

Six Steps Toward Improving Discoverability of Ph.D. Dissertations

This study proposes six steps that scientific institutions should undertake to increase the visibility and accessibility of their dissertations; these steps were implemented in the digital library of the University of Novi Sad. An analysis was conducted thereafter to evaluate the success, and it was found that the six steps and associated strategies were successful, with 400,000 downloads having been performed since the digital library was operational. Although this study presents six steps for improving electronic thesis and dissertation (ETD) discoverability in the digital library at the University of Novi Sad, these steps can easily be customized and implemented for ETD digital libraries at any scientific institution

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF