How to Elaborate a Demonstrator in Inspiring Science Education Platform

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Stability of an Optimal Schedule for a Job-Shop Problem with Two Jobs

The usual assumption that the processing times of the operations are known in advance is the strictest one in scheduling theory. This assumption essentially restricts practical aspects of deterministic scheduling theory since it is not valid for the most processes arising in practice. The paper is devoted to a stability analysis of an optimal schedule, which may help to extend the significance of scheduling theory for decision-making in the real-world applications. The term stability is generally used for the phase of an algorithm, at which an optimal solution of a problem has already been found, and additional calculations are performed in order to study how solution optimality depends on variation of the numerical input data.

Handling the Subclassing Anomaly with Object Teams

Java software or libraries can evolve via subclassing. Unfortunately, subclassing may not properly support code adaptation when there are dependencies between classes. More precisely, subclassing in collections of related classes may require reimplementation of otherwise valid classes. This problem is defined as the subclassing anomaly, which is an issue when software evolution or code reuse is a goal of the programmer who is using existing classes. Object Teams offers an implicit fix to this problem and is largely compatible with the existing JVMs. In this paper, we evaluate how well Object Teams succeeds in providing a solution for a complex, real world project. Our results indicate that while Object Teams is a suitable solution for simple examples, it does not meet the requirements for large scale projects. The reasons why Object Teams fails in certain usages may prove useful to those who create linguistic modifications in languages or those who seek new methods for code adaptation.

The Development of the Generalization Algorithm Based on the Rough Set Theory

This paper considers the problem of concept generalization in decision-making systems where such features of real-world databases as large size, incompleteness and inconsistence of the stored information are taken into account. The methods of the rough set theory (like lower and upper approximations, positive regions and reducts) are used for the solving of this problem. The new discretization algorithm of the continuous attributes is proposed. It essentially increases an overall performance of generalization algorithms and can be applied to processing of real value attributes in large data tables. Also the search algorithm of the significant attributes combined with a stage of discretization is developed. It allows avoiding splitting of continuous domains of insignificant attributes into intervals.

Training a Linear Neural Network with a Stable LSP Solution for Jamming Cancellation

Two jamming cancellation algorithms are developed based on a stable solution of least squares problem (LSP) provided by regularization. They are based on filtered singular value decomposition (SVD) and modifications of the Greville formula. Both algorithms allow an efficient hardware implementation. Testing results on artificial data modeling difficult real-world situations are also provided.

Timed Transition Automata as Numerical Planning Domain

A general technique for transforming a timed finite state automaton into an equivalent automated planning domain based on a numerical parameter model is introduced. Timed transition automata have many applications in control systems and agents models; they are used to describe sequential processes, where actions are labelling by automaton transitions subject to temporal constraints. The language of timed words accepted by a timed automaton, the possible sequences of system or agent behaviour, can be described in term of an appropriate planning domain encapsulating the timed actions patterns and constraints. The time words recognition problem is then posed as a planning problem where the goal is to reach a final state by a sequence of actions, which corresponds to the timed symbols labeling the automaton transitions. The transformation is proved to be correct and complete and it is space/time linear on the automaton size. Experimental results shows that the performance of the planning domain obtained by transformation is scalable for real world applications. A major advantage of the planning based approach, beside of the solving the parsing problem, is to represent in a single automated reasoning framework problems of plan recognitions, plan synthesis and plan optimisation.

Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach

One of the ultimate aims of Natural Language Processing is to automate the analysis of the meaning of text. A fundamental step in that direction consists in enabling effective ways to automatically link textual references to their referents, that is, real world objects. The work presented in this paper addresses the problem of attributing a sense to proper names in a given text, i.e., automatically associating words representing Named Entities with their referents. The method for Named Entity Disambiguation proposed here is based on the concept of semantic relatedness, which in this work is obtained via a graph-based model over Wikipedia. We show that, without building the traditional bag of words representation of the text, but instead only considering named entities within the text, the proposed method achieves results competitive with the state-of-the-art on two different datasets.