Evaluating Go Game Records for Prediction of Player Attributes

We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or statistic of sente/gote sequences. Using machine learning algorithms, the evaluations can be utilized to predict different relevant target variables. We apply this methodology to predict the strength and playing style of the player (e.g. territoriality or aggressivity) with good accuracy. We propose a number of possible applications including aiding in Go study, seeding real-work ranks of internet players or tuning of Go-playing programs

The strategic impact of META-NET on the regional, national and international level

This article provides an overview of the dissemination work carried out in META-NET from 2010 until 2015; we describe its impact on the regional, national and international level, mainly with regard to politics and the funding situation for LT topics. The article documents the initiative's work throughout Europe in order to boost progress and innovation in our field.Peer ReviewedPostprint (author's final draft

Implementing structural funds in the new member states: ten policy challenges

Paper presented at the ‘Roundtable on Benchmarking the Ten New Member States’, organised by the Scottish Executive EU Office