Marios Poulos

Evaluating authoritative sources using social networks: an insight from Wikipedia

– The purpose of this paper is to present an approach to evaluating contributions in collaborative authoring environments, and in particular, Wikis using social network measures., – A social network model for Wikipedia has been constructed, and metrics of importance such as centrality have been defined. Data has been gathered from articles belonging to the same topic using a web crawler, in order to evaluate the outcome of the social network measures in the articles., – Finds that the question of the reliability regarding Wikipedia content is a challenging one and as Wikipedia grows, the problem becomes more demanding, especially for topics with controversial views such as politics or history., – It is believed that the approach presented here could be used to improve the authoritativeness of content found in Wikipedia and similar sources., – This work tries to develop a network approach to the evaluation of Wiki contributions, and approaches the problem of quality Wikipedia content from a social network point of view.

Using Online Consumer Reviews as a Source for Demographic Recommendations: A Case Study Using Online Travel Reviews

Online consumer reviews play an important role in the decision to purchase services online, mainly due to the rich information source they provide to consumers in terms of evaluating “experience”-type products and services that can be booked using the Internet, with online travel services being a significant example. However, different types of travelers assess each quality indicator differently, depending on the type of travel they engage in, and not necessarily their cultural or age background (e.g. solo travelers, young couples with children etc.). In this study, we present architecture for a demographic recommendation system, based on a user-defined hierarchy of service quality indicator importance, and classification of traveler types. We use an algebraic approach to ascertain preferences from a large dataset that we obtained from the popular travel website Booking.com using a web crawler and compared with the customer-constructed preference matrix. Interestingly, the architecture of the evaluated recommendation system takes into account already defined demand characteristics of the hotels (such as the number of reviews of specific consumer types compared to the total number of reviews) in order to provide an example architecture for a recommendation system based on user-defined preference criteria.

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.

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...

A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems

This paper focuses on solving the problems of preparing and normalizing data that are captured from a classroom observation, and are linked with significant relevant properties. We adapt these data using a Bayesian model that creates normalization conditions to a well fitted artificial neural network. We separate the method in two stages: first implementing the data variable in a functional multi-factorial normalization analysis using a normalizing constant and then using constructed vectors containing normalization values in the learning and testing stages of the selected learning vector quantifier neural network.

Audio Fingerprint Extraction Using an Adapted Computational Geometry Algorithm

This work presents an adapted version of the Computational Geometry Algorithm (CGA) used for the development of audio-based applications and services. The CGA algorithm analyses an audio stream and produces a unique set of points that can be considered to be the audio data “fingerprint”. It is shown that this fingerprint is coding-independent, a fact that can render the proposed algorithm suitable for multiple purposes, including the categorisation of content identity and the identification of audio clips, hence providing support for the realisation of audio sorting/searching tasks and services. Additionally, based on specific novel applications and services, the overall algorithmic performance and efficiency characteristics of the CGA algorithm are discussed and analysed.

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.

Social metadata for the impact factor

– The purpose of this research is to address the need for a definition of metadata descriptors for use in enhancing the accuracy of bibliometric instruments of scholarly evaluation, such as the impact factor., – A semantic vocabulary – COAP – is constructed, deployed on top of the Resource Description Framework (RDF), by extending the Friend‐of‐a‐Friend (FOAF) schema., – An extension of the FOAF vocabulary is considered as the ability to describe a publication record such as this paper in terms of scholar contributions and participations. In order to achieve that, the FOAF vocabulary is extended., – The application of this semantic vocabulary could be used as a way of enhancing the accuracy of source data for bibliometric evaluation instruments., – The paper discusses how metadata descriptors can contribute to the improvement of already established scholar evaluation instruments such as the impact factor. It will be of use in the development of digital libraries.

Knowledge-based system for prognosis of specific types of cancer using Elman neural network

This paper focuses on the classification problem of high dimensional patterns and especially of socio-demographic cancer questionnaires. The purpose of this study is to define a predictive indicator of a published clinical study regarding the influence of Hormone Replacement Therapy (HRT) on the growth of cancers, including breast, ovarian, endometrial, and colon cancers. The proposed study, in the preparation stage, combines independent factors of this research using a Bayesian model in order to achieve a normalizing data linked by significant relevant properties of these factors. The specific goal is to determine a standard threshold value in which an independent self-organizing system will decide the correlation between the normalizing data of the preprocessing stage via a well-fitted, recurrent Elman neural network using a threshold factor which is called the distance value. A case study involving a dataset of published clinical research is used and the evaluated procedure is implemented by a well-fitted t-test control.

Is it possible to extract a fingerprint for early breast cancer via EEG analysis

People with abnormal breast tissue can eventually present breast cancer symptoms. The aim of this paper is to examine new prognostic methods for breast cancer using electroencephalographic techniques (EEG). Since it is known that electrical brain activity can be recorded by EEG, and since proteins and genes affect the electrical activity of the brain via the control of the flow of Na(+) and K(+) ions, and also given the fact that breast cancer and many other types of cancer are associated with 14-3-3 protein and genes, we hypothesize that there is a relationship between breast cancer and EEG. We examine the theoretical linkages between EEG, genes and proteins, and breast cancer, and propose the development of an intelligent technique for associating EEG with the early stages of breast cancer (i.e., atypical hyperplasia) as a fingerprint characteristic. This novel approach may provide the means for a new diagnostic and prognostic approach to early stage breast cancer, and other cancers.