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Serdica Journal of Computing has completed its first decade.On this occasion we offer some notes, statistical and otherwise, on the firstten volumes of the journal, concerning the number of authors and papers,the geographical and institutional distribution of authors’ affiliations, thestructural semantic makeup, the dynamics of the editorial board, and variousimportant developments in this period.ACM Computing Classification System (1998): A.0.

This special double issue of Serdica Journal of Computing is dedicated to the 80 th anniversary of the birthday of Professor Edward A. Friedman, internationally renowned scientist and innovative educator, versatile humanitarian, and member of our editorial board.

This special double issue of Serdica Journal of Computing is dedicated to the 80 th anniversary of the birthday of Professor Edward A. Friedman, internationally renowned scientist and innovative educator, versatile humanitarian, and member of our editorial board.

Stochastic arithmetic has been developed as a model for exact computing with imprecise data. Stochastic arithmetic provides confidence intervals for the numerical results and can be implemented in any existing numerical software by redefining types of the variables and overloading the operators on them. Here some properties of stochastic arithmetic are further investigated and applied to the computation of inner products and the solution to linear systems. Several numerical experiments are performed showing the efficiency of the proposed approach. * The third author was partially supported by the Bulgarian NSF Project DO 02-359/2008.

Stochastic arithmetic has been developed as a model for computing with imprecise numbers. In this model, numbers are represented by independent Gaussian variables with known mean value and standard deviation and are called stochastic numbers. The algebraic properties of stochastic numbers have already been studied by several authors. Anyhow, in most life problems the variables are not independent and a direct application of the model to estimate the standard deviation on the result of a numerical computation may lead to some overestimation of the correct value. In this work “quasilinear” algebraic structures based on standard stochastic arithmetic are studied and, from pure abstract algebraic considerations, new arithmetic operations called “inner stochastic addition and subtraction” are introduced. They appear to be stochastic analogues to the inner interval addition and subtraction used in interval arithmetic. The algebraic properties of these operations and the involved algebraic structures are then studied. Finally, the connection of these inner operations to the correlation coefficient of the variables is developed and it is shown that they allow the computation with non-independent variables. The corresponding methodology for the practical application of the new structures in relation to problems analogous to “dependency problems” in interval arithmetic is given and some numerical experiments showing the interest of these new operations are presented. ACM Computing Classification System (1998): D.2.4, G.3, G.4.

This paper is a reflection on the history and future of technology-enhanced learning. Over the last century various new technologies were introduced in education. Often, educational revolutions were proclaimed. Unfortunately, most of these new technologies failed to meet the high expectations. This paper reviews the rise and fall of various "revolutionary" learning technologies and analyses what went wrong. Three main driving factors are identified that influence the educational system: 1) educational practice, 2) educational research, and 3) educational technology. The role and position of these factors is elaborated and critically reviewed. Today, again many promising new technologies are being put in place for learning: gaming, social web, and mobile technologies, for example. Inevitably, these are once again proclaimed by its supporters to revolutionise teaching and learning. The paper concludes with identifying a number of relevant factors that substantiate a favourable future outlook of technology-enhanced learning.

This special issue of Serdica Journal of Computing is aimed at presenting some current trends in Bioinformatics research and especially the impact of application of advanced Computer Science and Artificial Intelligence approaches to building novel Computational Biology models, techniques and tools. At the same time, one of the main goals of this issue is to present the interdisciplinary and inter-professional research in Bioinformatics in an understandable form and thus make its results accessible and attractive for the general computing-oriented audience.

This special issue of Serdica Journal of Computing is aimed at presenting some current trends in Bioinformatics research and especially the impact of application of advanced Computer Science and Artificial Intelligence approaches to building novel Computational Biology models, techniques and tools. At the same time, one of the main goals of this issue is to present the interdisciplinary and inter-professional research in Bioinformatics in an understandable form and thus make its results accessible and attractive for the general computing-oriented audience.

We propose an algorithm that computes the length of a longest path in a cactus graph. Our algorithm can easily be modified to output a longest path as well or to solve the problem on cacti with edge or vertex weights. The algorithm works on rooted cacti and assigns to each vertex a two-number label, the first number being the desired parameter of the subcactus rooted at that vertex. The algorithm applies the divide-and-conquer approach and computes the label of each vertex from the labels of its children. The time complexity of our algorithm is linear in the number of vertices, thus improving the previously best quadratic time algorithm. The work performed by this author was partially funded by the Romanian National Council for Scientific Research (CNCS)-UEFISCDI under research grant PD_240/2010 (AATOMMS – contract no. 33/28.07.2010), from the PN II – RU program, and by the Sectoral Operational Programme Human Resources Development 2007-2013 of the Romanian Ministry of Labour, Family and Social Protection through the financial agreement POSDRU/89/1.5/S/62557. ACM Computing Classification System (1998): G.2.2.

The paper presents a computational analysis of Bulgarian dialect variation, concentrating on pronunciation differences. It describes the phonetic data set compiled during the project* ‘Measuring Linguistic Unity and Diversity in Europe’ that consists of the pronunciations of 157 words collected at 197 sites from all over Bulgaria. We also present the results of analyzing this data set using various quantitative methods and compare them to the traditional scholarship on Bulgarian dialects. The results have shown that various dialectometrical techniques clearly identify east-west division of the country along the ‘jat’ border, as well as the third group of varieties in the Rodopi area. The rest of the groups specified in the traditional atlases either were not confirmed or were confirmed with a low confidence. * The project is sponsored by Volkswagen Stiftung. More information can be found at http://www.sfs.uni-tuebingen.de/dialectometry