Sozon Papavlasopoulos

Music, musicians and information seeking behaviour

Purpose – The purpose of this paper is to examine information seeking behaviour targeted to music information seeking by amateur musicians, accompanied with empirical evidence from a survey on a community concert band. While several studies in the literature have examined information seeking in the context of hedonic motives (e.g. entertainment oriented), music information can also be used for utilitarian purposes by providing amateur musicians the necessary tools to improve their skill and become better in their practice. Design/methodology/approach – A review of the literature on music information seeking and an empirical study on members of an amateur concert band are presented. The theoretical construct of the survey is informed by Wilsons’ macro model of information seeking behaviour. This is employed in order to understand information motives and needs, as well as obstacles in information seeking of musicians. Findings – Musicians seek information not only for entertainment but for educational purpo...

Specific Selection of FFT Amplitudes from Audio Sports and News Broadcasting for Classification Purposes

In this paper we investigate the problem of classification between sports and news broadcasting. We detect and classify files that consist of speech and music or background noise (news broadcasting), and speech and a noisy background (sports broadcasting). More specifically, this study investigates feature extraction and training and classification procedures. We compare the Average Magnitude Difference Function (AMDF) method, which we consider more robust to background noise, with a novel proposed method. This method uses several spectral audio features which may be considered as specific semantic information. We base the extraction of these features on the theory of computational geometry using an Onion Algorithm (OA). We tested the classification procedure as well as the learning ability of the two methods using a Learning Vector Quantizer One (LVQ1) neural network. The results of the experiment showed that the OA method has a faster learning procedure, which we characterise as an accurate feature extraction method for several audio cases.

Responding to Crises with Alliances: Evidence from an Academic Library Survey in Greece

The global recession which began in 2008 affected the entire world including the European economy, with some countries being influenced more than others. At the end of 2012 the Greek economy was encountering a fourth consecutive year of deep recession while pressures to cut expenses in all sectors were still growing and making headline news. Academic libraries, which are dependent upon state funding, were experiencing the consequences of constant and deep budget cutbacks during that period. After a review the literature on the impact of the economic crisis on academic libraries in Greece, as well as at the international level, this chapter describes the results of a survey of Greek higher education academic libraries about the consequences of the devastatingly harsh economic environment in which they currently, and probably will continue to, exist. A survey was conducted online with 25 out of the 37 academic library directors in Greece. After analyzing the survey results, the authors describe strategies to sustain services and resources and propose strategies to adjust to a new fiscal reality. These strategies include synergies and alliances that academic libraries can achieve with various agencies within their educational institutions and/or externally. While the results are limited to a small number of academic libraries in one European country, all types of libraries can utilize the strategies outlined in this chapter.

Neural network design and evaluation for classifying library indicators using personal opinion of expert

Purpose – Over the past decades scientific advances have inspired major technological innovations in academic libraries. Academic libraries have transformed into information centers of new quality, becoming thus an integral part of an academic institutions teaching and research curriculum. Assessing a librarys functions and services has become an imperative need. The scope of this study, therefore, is to define a theoretical model for the combination of all individual assessment indicators into one single number‐indicator.Design/methodology/approach – The Monte Carlo technique is suggested in order to deal with the problems of the objectivity experts decision and the collection of a large number of indicators. The methodology used is articulated into four stages: the normalization of single values; the setting of the indicator weighting according to the experts opinion; the construction of a well‐fitted neural network; and the training and testing procedure of the neural network.Findings – The propose...

Towards Constructing a Bibliometric Cited Distance Factor for an Article Using an Elman Neural Network

In this paper we attempt to construct an ideal factor for the evaluation of an article, which combines objectivity with the ability to bridge many different bibliometric factors. The aim of this study is to determine a standard threshold value with which an independent, self-organizing system can determine the correlation between the value and the citation score of an article. This factor is called Cited Distance and is extracted via a well-fitted recurrent neural network. We use articles from the 10 cell biology journals in the Web of Science database with the highest impact factors for the years 1995–2007.

Short Text Coherence Hypothesis

Abstract In this paper, we experimentally study the degree to which the length of a short text affects its comprehensiveness and readability, within quantitative linguistics. The quantitative linguistics focus mainly in analysis of large text collections and one of the major scientific theories in use is the Menzerath-Altmann law. In this paper we attempt to define the quantitative analysis framework for short texts consisting approximately of one or two sentences, due to the fact that they are considered very important in many scientific fields. To achieve the aim of this paper, a coherence statistical testing process of three variables was created for short texts. The implementation of that was possible through experimental and statistical evaluation. Upon completion of the above-mentioned evaluation, the statistical results showed that short text coherence, comprehensiveness and readability are fully achieved in short texts consisting of 14 words, when three predetermined variables are associated and vice versa. To prove the above hypothesis the theory of Vector Space Model and Kendall’s Coefficient of Concordance were used. The assessment of statistical results concluded that the above hypothesis can be fully met for a number of cases with a probability p > 99%. Moreover, in the experiment were used short texts in English language but it was proven that language can be considered irrelevant. To corroborate this, a smaller scale experiment with short texts in the German language was conducted and hypothesis was confirmed that the proposed model of this paper can be applied in all short texts regardless of their linguistic origin.

Biobibliometrics & gene connections

In recent years there has been an increasingly amount of data stored in biomedical Databases. The information provided by scientist is stored but it is difficult to be analyzed based on specific parameters. New Scientific fields as Bioinformatics seem to be the tool needed to extract scientifically important data based on experimental results and information provided by papers and journals. In this paper we will propose a solution to the problem in hand, based on biobliometrics, statistical analysis and Bioinformatics. The proposed system could become a useful tool against the struggle of scientists and medical professionals in the near future.

A Dynamic Identification of a Historical Building Using Accelerometers with Interface Modules and a Digital Synchronization Method

This paper describes a study about the San Giacomo building for testing the dynamic identification applicability of a low-cost monitoring system, consisting of accelerometers and acquisition modules. The Stochastic Subspace Identification (SSI), a well-known technique of Operational Modal Analysis (OMA), is applied to the experimental data to evaluate the possibility of identifying the first frequencies of the building. Moreover, in order to solve the lack of synchronization of the monitoring system, an innovative method based on the phase delay of each signal is presented and used for digitally synchronizing the data.

Automatic stationary detection of time series using auto-correlation coefficients and LVQ — Neural network

A data mining of Time Series using Autocorrelation Coefficients (ACC) and LVQ -Neural Network is addressed in this work-a problem that has not yet been seen in a signal processing framework, to the best of our knowledge. Neural network classification was performed on real Time series Data of real data, in an attempt to experimentally investigate the connection between Time Series data and hidden information about the properties of stationary Time Series. Finally, the ability of the ACC will be tested via a well fitted LVQ neural network which gives satisfactory results in predicting Time Series.

A Web Classifier for Semantic Classification Between News and Sports Broadcasts

Lately, lot of work has been done in the area of content-based audio classification. In this paper we experiment on audio classification between sports and news broadcasts using the Average Magnitude Difference Function as the feature extractor and an LVQ1 neural network as classifier. The method proves robust and the results are reliable and could be further utilized in an automated web classifier.