Applications of Nonclassical Logic Methods for Purposes of Knowledge Discovery and Data Mining

* The work is partially supported by Grant no. NIP917 of the Ministry of Science and Education – Republic of Bulgaria.

Evolutionary Clustering of Complex Systems and Processes

In a paper the method of complex systems and processes clustering based use of genetic algorithm is offered. The aspects of its realization and shaping of fitness-function are considered. The solution of clustering task of Ukraine areas on socio-economic indexes is represented and comparative analysis with outcomes of classical methods is realized.

Evolution of Sets Systems and Homotopy Groups of Spheres

Симеон Т. Стефанов, Велика И. Драгиева - В работата е изследвана еволюцията на системи от множества върху n-мерната евклидова сфера S^n. Установена е връзката на такива системи с хомотопичните групи на сферите. Получени са някои комбинаторни приложения за многостени.

Fourth National Information Day and Workshop: Open Access to Scientific Information and Data

The paper presents in brief the “2nd Generation Open Access Infrastructure for Research in Europe” project (http://www.openaire.eu/) and what is done in Bulgaria during the last year in the area of open access to scientific information and data.

Viability, Optimality and Stability of Dynamical Systems and Estimation of Convergence of Numerical Schemes

AMS subject classification: 49N35,49N55,65Lxx.

Symbolic Solving of Partial Differential Equation Systems and Compatibility Conditions

An algorithm is produced for the symbolic solving of systems of partial differential equations by means of multivariate Laplace–Carson transform. A system of K equations with M as the greatest order of partial derivatives and right-hand parts of a special type is considered. Initial conditions are input. As a result of a Laplace–Carson transform of the system according to initial condition we obtain an algebraic system of equations. A method to obtain compatibility conditions is discussed.

Analysis and Data Mining of Lead-Zinc Ore Data

This paper presents the results of our data mining study of Pb-Zn (lead-zinc) ore assay records from a mine enterprise in Bulgaria. We examined the dataset, cleaned outliers, visualized the data, and created dataset statistics. A Pb-Zn cluster data mining model was created for segmentation and prediction of Pb-Zn ore assay data. The Pb-Zn cluster data model consists of five clusters and DMX queries. We analyzed the Pb-Zn cluster content, size, structure, and characteristics. The set of the DMX queries allows for browsing and managing the clusters, as well as predicting ore assay records. A testing and validation of the Pb-Zn cluster data mining model was developed in order to show its reasonable accuracy before beingused in a production environment. The Pb-Zn cluster data mining model can be used for changes of the mine grinding and floatation processing parameters in almost real-time, which is important for the efficiency of the Pb-Zn ore beneficiation process. ACM Computing Classification System (1998): H.2.8, H.3.3.

The Fifth National Information Day: Open Access to Scientific Information and Data

This work was supported in part by the EU „2nd Generation Open Access Infrastructure for Research in Europe" (OpenAIRE+). 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.

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.

Conceptual Model and Security Requirements for DRM Techniques Used for e-Learning Objects Protection

This paper deals with the security problems of DRM protected e-learning content. After a short review of the main DRM systems and methods used in e-learning, an examination is made of participators in DRM schemes (e-learning object author, content creator, content publisher, license creator and end user). Then a conceptual model of security related processes of DRM implementation is proposed which is improved afterwards to reflect some particularities in DRM protection of e-learning objects. A methodical way is used to describe the security related motives, responsibilities and goals of the main participators involved in the DRM system. Taken together with the process model, these security properties are used to establish a list of requirements to fulfill and a possibility for formal verification of real DRM systems compliance with these requirements.

Data Protection and Packet Mode in the Distributed Information Measurement and Control System for Research in Physics

The present paper is devoted to creation of cryptographic data security and realization of the packet mode in the distributed information measurement and control system that implements methods of optical spectroscopy for plasma physics research and atomic collisions. This system gives a remote access to information and instrument resources within the Intranet/Internet networks. The system provides remote access to information and hardware resources for the natural sciences within the Intranet/Internet networks. The access to physical equipment is realized through the standard interface servers (PXI, CАМАC, and GPIB), the server providing access to Ethernet devices, and the communication server, which integrates the equipment servers into a uniform information system. The system is used to make research task in optical spectroscopy, as well as to support the process of education at the Department of Physics and Engineering of Petrozavodsk State University.

Organization and Security of the Audio and Video Archive for Unique Bulgarian Bells

AMS Subj. Classification: H.3.7 Digital Libraries, K.6.5 Security and Protection

The Design and Analysis of Context-Aware, Secure Workflow Systems

Workflows are set of activities that implement and realise business goals. Modern business goals add extra requirements on workflow systems and their management. Workflows may cross many organisations and utilise services on a variety of devices and/or supported by different platforms. Current workflows are therefore inherently context-aware. Each context is governed and constrained by its own policies and rules to prevent unauthorised participants from executing sensitive tasks and also to prevent tasks from accessing unauthorised services and/or data. We present a sound and multi-layered design language for the design and analysis of secure and context aware workflows systems.