In the literature studentification is closely associated with gentrification. Many authors consider the mass invasion of students to inner-city neighbourhoods as a type of gentrification, some of them even use the two terms interchangeably. Regardless of theoretical considerations, it seems that there is a consensus on the significance of studentification in contemporary urban transformations. Recent studies demonstrate that the appearance of students as 'transient' inhabitants in inner-city neighbourhoods, accompanied by growing differences in lifestyles and socio-cultural attitudes weakens social cohesion in the affected neighbourhoods Haase, A. et al. 2012). This study focuses on the interplay between studentification, socio-cultural diversity and social cohesion in an inner-city district of Budapest, Józsefváros. The district has been characterised by several waves of urban rehabilitation and subsequent gentrification since the transition of [1989][1990]. Studentification has also become dominant phenomenon in the area due to the settling of new higher education institutions, the concomitant influx of students, as well as low rent and dwelling prices providing a niche for the spontaneous movements of a new generation of urbanites (Grabkowska, M. 2011;Bernt, M. et al. 2015). The main research questions we intend to answer in this paper are as follows. Firstly, what is the relationship between studentification and gentrification in Józsefváros? Secondly, what kinds of effects has the influx of students on the social cohesion of the district? According to our findings, studentification in Józsefváros is not the pioneer phase of gentrification, but -especially in the rehabilitation areas -it runs more or less parallel with it. Most of the students use the place as a springboard in their career, and after graduation most of them leave. The attitude of students is, however, very much resembles those of the young, highly educated newcomers, i.e. early gentrifiers. The impacts of studentification on the social milieu of Józsefváros are contradictory. On the one hand, the inflow of students and young intellectuals increases the socio-cultural diversity of the district, contributing to the recognition of diversity in a post-socialist context, and fostering tolerance and the acceptance of different cultures and lifestyles. On the other hand, it seems that the ongoing population change hampers the establishment of strong social ties at the neighbourhood level and leads to certain conflicts.

Public spaces are spaces for representation of individuals, social groups, ideologies, values, cultures. At the same time, public spaces are commodifi ed and "disneyfi ed" in capitalism, thus, the creation of a homogeneous, "desirable" spatial form and use of public space is often intended. Therefore, their production is characterised by constant rivalries and struggles and causes social confl icts. In addition, the production of public spaces in post-socialism has distinct development paths which is often characterised by contradicting traditions and objectives regarding the spatial form and the everyday practices taking place there. These contradictions create confl icts between various individuals, social groups, actors of urban policies etc. The aim of the paper is to investigate how urban social diversity is (re)presented in the public spaces of the 8 th district of Budapest, Józsefváros. Which groups and values are more visible than others? How do local people use public spaces? The research is based on the content analysis of policy documents and interviews conducted with local residents of Józsefváros -one of the most diverse districts of Budapest. According to our results, the fragmented nature of the local society is refl ected in the use of public space: diversity is present between the public spaces and not within them. Furthermore, some of the recent developments support the homogenisation of values and behaviours in public spaces.

The application of Articial Intelligence (AI) in the Software Engineering (SE) eld is always a bit delayed compared to state-ofthe-art research results. While the Generative Pre-trained Transformer (GPT-2) model was published in 2018, only a few recent works used it to SE tasks. One of such task is Automated Program Repair (APR), where the applied technique should nd a x to software bugs without human intervention. One problem emerges here: the creation of proper training data is resource intensive and requires several hours of additional work from researchers. The sole reason of it is that training a model to repair programs automatically requires both the buggy program and the xed one in large scale and presumably in an already pre-processed form. There are currently few such databases, so teaching and ne-tuning models is not an easy task. In this work we wanted to investigate how the GPT-2 model performs when it is not ne-tuned for the APR task, compered to when it is ne-tuned. From previous work we already know that the GPT-2 model can automatically generate patches for buggy programs, although the literature lacks of studies where no ne-tuning has taken place. For the sake of experiment we evaluated the GPT-2 model out-of-the-box and also ne-tuned it before the evaluation on 1559 JavaSript code snippets. Based on out results we can conclude that although the ne-tuned model was able to learn how to write syntactically correct source code almost on every attempt, the non-ne-tuned model lacked some of these positive features.

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