Determinants of civil society influence:the case of international development and humanitarian NGOs in the Czech Republic and Hungary

Accession to the EU has had ambiguous effects on civil society organizations (CSOs) in the East Central European countries. A general observation is that accession has not led to the systematic empowerment of CSOs in terms of growing influence on national policy making. This article investigates the determinants of successful CSO advocacy by looking at international development and humanitarian NGOs (NGDOs) in the Czech Republic and Hungary. Reforms in the past decade in the Czech Republic have created an international development policy largely in line with NGDO interests, while Hungary’s ministry of foreign affairs seems to have been unresponsive to reform demands from civil society. The article argues that there is clear evidence of NGDO influence in the Czech Republic on international development policy, which is because of the fact that Czech NGDOs have been able solve problems of collective actions, while the Hungarian NGDO sector remains fragmented. They also have relatively stronger capacities, can rely on greater public support and can thus present more legitimate demands towards their government.

Observation of Matthew effects in Sina Weibo microblogger

This paper researches on Matthew Effect in Sina Weibo microblogger. We choose the microblogs in the ranking list of Hot Microblog App in Sina Weibo microblogger as target of our study. The differences of repost number of microblogs in the ranking list between before and after the time when it enter the ranking list of Hot Microblog app are analyzed. And we compare the spread features of the microblogs in the ranking list with those hot microblogs not in the list and those ordinary microblogs of users who have some microblog in the ranking list before. Our study proves the existence of Matthew Effect in social network. © 2013 IEEE.

Using observation ageing to improve Markovian model learning in QoS engineering

Markovian models are widely used to analyse quality-of-service properties of both system designs and deployed systems. Thanks to the emergence of probabilistic model checkers, this analysis can be performed with high accuracy. However, its usefulness is heavily dependent on how well the model captures the actual behaviour of the analysed system. Our work addresses this problem for a class of Markovian models termed discrete-time Markov chains (DTMCs). We propose a new Bayesian technique for learning the state transition probabilities of DTMCs based on observations of the modelled system. Unlike existing approaches, our technique weighs observations based on their age, to account for the fact that older observations are less relevant than more recent ones. A case study from the area of bioinformatics workflows demonstrates the effectiveness of the technique in scenarios where the model parameters change over time.