George Kampis

Beyond the basemap of science: mapping multiple structures in research portfolios: evidence from Hungary

As a novel tool for evaluating research competences of R&D actors, science overlay maps have recently been introduced in the scientometric literature, with associated measures for assessing the degree of diversification in research profiles. In this study, we continue the elaboration of this approach: based on science overlay maps (called here m-maps), a new type of map is introduced to reveal the competence structure of R&D institutions (i-maps). It is argued, that while m-maps represent the multidisciplinarity of research profiles, i-maps convey the extent of interdisciplinarity realized in them. Upon i-maps, a set of new measures are also proposed to quantify this feature. With these measures in hand, and also as a follow-up to our previous work, we apply these measures to a sample of Hungarian Research Institutions (HROs). Based on the obtained rankings, a principal component analysis is conducted to reveal main structural dimensions of researh portfolios (of HROs) covered by these measures. The position of HROs along these dimensions then allows us to draw a typology of organizations, according to various combinations of inter- and multidisciplinarity characteristic of their performance.

Collaborative Knowledge Fusion by Ad-Hoc Information Distribution in Crowds

We study situations where (such as in a city festival) in the case of a phone signal outage cell phones can communicate opportunistically (for instance, using WiFi or Bluetooth) and we want to understand and control information spreading. A particular question is, how to prevent false information from spreading, and how to facilitate the spreading of useful (true) information? We introduce collaborative knowledge fusion as the operation by which individual knowledge claims are “merged”. Such fusion events are necessarily local, e.g. happen upon the physical meetings of knowledge providers. We study and evaluate different methods for collaborative knowledge fusion and study the conditions for and tradeoffs of the convergence to a global true knowledge state under various conditions.

Analytical and simulation models for collaborative localization

Abstract Collaborative localization is a special case for knowledge fusion where information is exchanged in order to attain improved global and local knowledge. We propose analytical as well as agent based simulation models for pedestrian dead reckoning (PDR) systems in agents collaborating to improve their location estimate by exchanging subjective position information when two agents are detected close to each other. The basis of improvement is the fact that two agents are at approximately the same position when they meet, and this can be used to update local position information. In analytical models we find that the localization error remains asymptotically finite in infinite systems or when there is at least one immobile agent (i.e. an agent with a zero localization error) in the system. In the agent model we tested finite systems under realistic (that is, inexact) meeting conditions and tested localization errors as function of several parameters. We found that a large finite system comprising hundreds of users is capable of collaborative localization with an essentially constant error under various conditions. The presented models can be used for predicting the improvement in localization that can be achieved by a collaboration among several mobile computers. Besides, our results can be considered as first steps toward a more general collaborative (incremental) form of knowledge fusion.

Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory

Abstract This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the authors’ keywords. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization(NMF) topic modeling algorithm to discover topics and labeled and grouped results hierarchically. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics.

An Adaptive and Dynamic Simulation Framework for Incremental, Collaborative Classifier Fusion

To investigate incremental collaborative classifier fusion techniques, we have developed a comprehensive simulation framework. It is highly flexible and customizable, and can be adapted to various settings and scenarios. The toolbox is realized as an extension to the NetLogo multi-agent based simulation environment using its comprehensive Java-API. The toolbox has been integrated in two different environments, one for demonstration purposes and another, modeled on persons using realistic motion data from Zurich, who are communicating in an ad hoc fashion using mobile devices.

Analysis of publication activity of computational science society in 2001–2017 using topic modelling and graph theory

Abstract This paper presents the results of topic modelling and analysis of topic networks using the corpus of the International Conference on Computational Science (ICCS), which contains 5982 domain-specific papers over seventeen years 2001–2017. We discuss the topical structures of ICCS, and show how these topics have evolved over time in response to the topicality of various domains, technologies and methods, and how all these topics relate to one another. This analysis illustrates the multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modelling results and from the authors’ keywords. The results of this study provide insights regarding the past and future trends of core discussion topics in computational science and show how “computational thinking” has propagated across different fields of study. We used the Non-negative Matrix Factorization (NMF) topic modelling algorithm to discover topics. The resulting topics were then manually labelled and grouped hierarchically on three levels. Next, we applied trend analysis and Change Point Analysis (CPA) to study the evolution of topics over seventeen years and to identify the growing and disappearing topics. We used Gephi to examine the static networks of topics, and an R library called DyA to analyse the dynamic networks of topics. We also analysed the conference as a platform for potential collaboration development through the perspective of collaboration networks. The results show that authors of ICCS papers continue to actively collaborate after the conference − on average authors collaborate with three other ICCS authors, − which suggests that ICCS is a valuable platform for collaboration development.

Incremental classifier fusion for smart societies

We present an abstract approach to incremental knowledge fusion (classifier fusion) with three different local update rules applied when agents meet. These are: a rule based on the averaging of local information, experience based reputation and transitive reputation, respectively. We introduce and discuss the role of Well Informed Agents (WIAs) in these systems. We analyze each rule in detail and present a comparison that reveals important differences. In particular, best convergence (but with a medium error term) is achieved by the transitive method, whereas middle values of convergence with the smallest error terms are shown by the averaging method. Experience based reputation fares worse of the three, both in terms of convergence speed and error. We discuss consequences for smart societies and directions of future work.