Impact on the fate of the euro area economy

Regarding the impact of EMU on euro area firms' competitiveness and their capacity to play an important role in international markets, recent research indicates Euro contribution to increasing the competitiveness of firms by facilitating trade between countries in the eurozone and by promoting the single market integration EU. Euro zone countries are considered to be the best export base, attracting a larger number of companies from neighboring countries. Euro area enlargement and better integrate it, in addition to allowing firms to diversify their operations to lower marginal costs, also tend to be more closely associated with competition and thus a higher variety of products, higher productivity and lower pricescompetitiveness, financial integration, market integration, foreign direct investment

Towards a Systematic Approach of Relational Database Watermarking

Nowadays more and more data of socio-technical systems become available online to anyone interested to access it or process it (without data alteration or copyright infringement). Generally, these data are stored in relational databases. However, to comply with this new paradigm new models of data access and security are necessary. One upcoming trend for relational databases is to watermark the database instance, i.e. to compute a secret code, which can be either embedded directly into the database or registered to a trusted authority. Current watermarking schemes only apply to either a particular database relation or index and, generally, distort the data. In this paper, we propose a methodology for distortion-free watermarking of both the database schema and instance that takes into account the database semantics, its dynamic, and also ensuring various security levels within the database. A possible scenario on using this methodology on a real-world database is also available

A Formal Description of the Systemic Theory based e-Learning

This paper aims at presenting the systems theory-based approach to the learning (instructional) process. Such as approach is necessary, if one takes into consideration the the complex and holistic nature of the learning process. The presented modeling is based on the definition of an automatic regulation system and artificial intelligence techniques. For this purpose, an Intelligent Instructional System (IIS) is defined using the concepts: inputs, outputs, perturbations factors, regulation, feedback and AI learning techniques. The contextual environment of learning plays the major role in this system and it establishes the perturbation factors. The regulation consists in AI learning techniques, so the IIS is a flexible and adaptive system. The process’ behaviour is described using pseudo-boolean and linear equations. This kind of approach enables solving the problems of the complexity and uncertainty of the learning (instructional) systems and, particularly, those of the e-learning (online and offline). Applications of the proposed approach may be found in e-learning courses for Mathematics, Computing, Architecture, Archaeology, Cultural Heritage, etc

Towards Building Creative Collaborative Learning Groups Using Reinforcement Learning

Increasing creative skills in collaborative groups is of huge interest for stakeholders in education, industry, policy making etc. However, construction of “the most” creative groups given a cohort of people and a set of common goals and tasks to perform is challenging. The complexity of this undertaking is amplified by the necessity to first understand and then measure what “the most” creative means in a particular situation. We present here our method of semi-automatic building of “the most” creative learning groups given a cohort of students and a particular learning context based on reinforcement learning (an adapted Q-learning algorithm). Various attributes that influence individual and group creativity may be considered. A case study on using this method with our Computer Science students is also included. However, the method is general and can be used for building collaborative groups in any situation, with the appropriate “the most” creative goal and attributes

Towards Construction of Creative Collaborative Teams Using Multiagent Systems

Group creativity and innovation are of chief importance for both collaborative learning and collaborative working, as increasing the efficiency and effectiveness of groups of individuals performing together specific activities to achieve common goals, in given contexts, is of crucial importance nowadays. Nevertheless, construction of “the most” creative and innovative groups given a cohort of people and a set of common goals and tasks to perform is challenging. We present here our method for semi-automatic construction of “the most” creative and innovative teams given a group of persons and a particular goal, which is based on unsupervised learning and it is supported by a multiagent system. Individual creativity and motivation are both factors influencing group creativity used in the experiments performed with our Computer Science students. However, the method is general and can be used for building the most creative and innovative groups in any collaborative situation

A Software System for Online Learning Applied in the Field of Computer Science

The computer-assisted learning is a very modern study area, which can be applied to the learning process. The main objective of this paper is to present a software system for online learning based on the intelligent software agents technologies. The main ideas on which this paper is built are: to any person is associated a learning profile (the idea is based on the existence of multiple intelligences, defined by Gardner [3]); the pedagogical resources can be shaped through educational semantic networks or through conceptual maps; a flexible software system in computer assisted learning must be based on the intelligent agents’ technology. The system dedicated to computer-assisted learning must be adapted to the learning profile of each student.The author presents a flexible online teaching software system, which learns to teach according to the learning profile of each student (the author defines this system in the PhD thesis and includes: intelligent agent structures, reward learning algorithms, algorithms to generate plans for an agent).The application includes two agents: the supervising agent and the pedagogical agent, which determines the optimal pedagogical resources for teaching the course. The application has been designed in Microsoft Visual Studio 6.0 and uses Microsoft Agent Technology, which allows vocal recognition. Also, the Protéjé 3.0 software has been used, software that allows building ontology for computer assisted learning. The system has been experimented on the Graph Theory Course, taught at postuniversitary computer science courses, the results proving the necessity of defining a strategy for selecting the pedagogical resources presented to the students according to their learning profile