The Ecce and Logen Partial Evaluators and their Web Interfaces

We present Ecce and Logen, two partial evaluators for Prolog using the online and offline approach respectively. We briefly present the foundations of these tools and discuss various applications. We also present new implementations of these tools, carried out in Ciao Prolog. In addition to a command-line interface new user-friendly web interfaces were developed. These enable non-expert users to specialise logic programs using a web browser, without the need for a local installation

Relational methodology for data mining and knowledge discovery

Knowledge discovery and data mining methods have been successful in many domains. However, their abilities to build or discover a domain theory remain unclear. This is largely due to the fact that many fundamental KDD&DM methodological questions are still unexplored such as (1) the nature of the information contained in input data relative to the domain theory, and (2) the nature of the knowledge that these methods discover. The goal of this paper is to clarify methodological questions of KDD&DM methods. This is done by using the concept of Relational Data Mining (RDM), representative measurement theory, an ontology of a subject domain, a many-sorted empirical system (algebraic structure in the first-order logic), and an ontology of a KDD&DM method. The paper concludes with a review of our RDM approach and \u27Discovery\u27 system built on this methodology that can analyze any hypotheses represented in the first-order logic and use any input by representing it in many-sorted empirical system

Symbolic methodology for numeric data mining

Currently statistical and artificial neural network methods dominate in data mining applications. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design, and other areas. Neural networks and decision tree methods have serious limitations in capturing relations that may have a variety of forms. Learning systems based on symbolic first-order logic (FOL) representations capture relations naturally. The learned regularities are understandable directly in domain terms that help to build a domain theory. This paper describes relational data mining methodology and develops it further for numeric data such as financial and spatial data. This includes (1) comparing the attribute-value representation with the relational representation, (2) defining a new concept of joint relational representations, (3) a process of their use, and the Discovery algorithm. This methodology handles uniformly the numerical and interval forecasting tasks as well as classification tasks. It is shown that Relational Data Mining (RDM) can handle multiple constrains, initial rules and background knowledge very naturally to reduce the search space in contrast with attribute-based data mining. Theoretical concepts are illustrated with examples from financial and image processing domains

Igniting technological modernization through science towns and technology parks: the case of Russia

Since the turn of the 21st century, the Russian state has attempted to address the country’s excessive dependence on natural resources. It has implemented an ambitious programme of economic modernization, including giving innovation more policy prominence and boosting state funding for research and development (R&D) and innovation. The programme includes a plethora of new initiatives, including innovation strategy documents, R&D funding for institutions, and state support for innovation infrastructure (e.g. clusters, science towns, and science and technology parks). However, despite investing substantial resources in science and technology since 2000 in a variety of forms and with an impressive legacy of scientific R&D from the Soviet period, Russia is still faring comparatively poorly in innovation outcomes, such as the number of innovative enterprises and international patent registrations. This thesis attempts to understand why Russia is performing comparatively poorly in innovation outcomes. It takes a multidisciplinary approach to examine why Russia is not doing as well in economic catch up and innovation as, for example, China. Following Taylor’s (2016) emphasis on the political economy of science, technology, and innovation policies, it suggests that a country’s political economy model is an important driver of innovation performance. The thesis finds that Russia has implemented a wide range of approaches to accelerate growth based on innovation and knowledge and provides new empirical material on Russia’s science towns and technology parks. Yet for all the good intentions and effort, Russia’s larger political economy model, as analysed here, has substantially hindered its rate of innovation and diffusion into commercial enterprises. The challenge of technological modernization is a matter of public concern and a problem to be solved by a diverse range of institutions and societal actors. Accordingly, technological modernization is enlightened by several conceptual perspectives. The five most helpful perspectives used in this thesis are certain modernization theories; rent-seeking (who benefits from modernization processes); neo-Schumpeterian and co-evolutionary growth approaches; innovation systems and innovation policies; and finally, sistema (Ledeneva, 2013), a political economic approach that explains key aspects of Russia’s current authoritarian system