Computer Mathematics with MuPAD in School Years 6. To 9

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Automatic Classification of Bulgarian Adjectives on Emotional Semantic Axes

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Dynamic Determination of Personalized Educational Paths

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Educational Objectives and Evaluation of Student's Internship and Practice in Informatics

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Challenges of Teaching Written Communication in the Cloud

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Documenting Collections of Bulgarian Museums. Framing a CIDOC-based Ontological Presentation

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Developing Financial Expertise Through Mathematics and IT School Curricula

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Autumn Training School. Development and Promotion of Open Access to Scientific Information and Research (September 19–20, 2014, Veliko Tarnovo, Bulgaria), organized in the frame of DiPP2014

The autumn training school Development and Promotion of Open Access to Scientific Information and Research is organized in the frame of the Fourth International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage—DiPP2014 (September 18–21, 2014, Veliko Tarnovo, Bulgaria, http://dipp2014.math.bas.bg/), organized under the UNESCO patronage. The main organiser is the Institute of Mathematics and Informatics, Bulgarian Academy of Sciences with the support of EU project FOSTER (http://www.fosteropenscience.eu/) and the P. R. Slaveykov Regional Public Library in Veliko Tarnovo, Bulgaria.

Key Aspects and Approaches of Open Access, Open Data and Open Science

Overview of the key aspects and approaches to open access, open data and open science, emphasizing on sharing scientific knowledge for sustainable progress and development.

Importance and Benefits of OPEN SCIENCE for Ubiquitous Sharing, Dissemination and Impact

While openness is well applied to software development and exploitation (open sources), and successfully applied to new business models (open innovation), fundamental and applied research seems to lag behind. Even after decades of advocacy, in 2011 only 50% of the public-funded research was freely available and accessible (Archambault et al., 2013). The current research workflows, stemming from a pre-internet age, result in loss of opportunity not only for the researchers themselves (cf. extensive literature on topic at Open Access citation project, http://opcit.eprints.org/), but also slows down innovation and application of research results (Houghton & Swan, 2011). Recent studies continue to suggest that lack of awareness among researchers, rather than lack of e-infrastructure and methodology, is a key reason for this loss of opportunity (Graziotin 2014). The session will focus on why Open Science is ideally suited to achieving tenure-relevant researcher impact in a “Publish or Perish” reality. Open Science encapsulates tools and approaches for each step along the research cycle: from Open Notebook Science to Open Data, Open Access, all setting up researchers for capitalising on social media in order to promote and discuss, and establish unexpected collaborations. Incorporating these new approaches into a updated personal research workflow is of strategic beneficial for young researchers, and will prepare them for expected long term funder trends towards greater openness and demand for greater return on investment (ROI) for public funds.

Open Access Strategy of UNESCO

One of UNESCO’s overarching goals is to build inclusive knowledge societies by harnessing information and communication technologies to maintain, increase and diffuse knowledge in the fields of education, the sciences, culture, and communication and information, including through open access. Open Access (OA) is the provision of free access to peer-reviewed, scholarly, research information (both scientific papers and research data) to all. It envisages that the rights-holder grants worldwide irrevocable right of access to copy, use, distribute, transmit, and make derivative works in any format for any lawful activities with proper attribution to the original author. Through Open Access, researchers and students from around the world gain increased access to knowledge, publications have greater visibility and readership, and the potential impact of research is heightened.

Recent Developments in Digital Mathematics Libraries

The paper presents recent developments in the domain of digital mathematics libraries towards the envisioned 21st Century Global Library for Mathematics. The Bulgarian Digital Mathematical Library BulDML and the Czech Digital Mathematical Library DML-CZ are founding partners of the EuDML Initiative and through it contribute to the sustainable development of the European Digital Mathematics Library EuDML and to the global advancements in this area.

Classification of Paintings by Artist, Movement, and Indoor Setting Using MPEG-7 Descriptor Features

ACM Computing Classification System (1998): I.4.9, I.4.10.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.