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Inference from data is of key importance in many applications of informatics. The current trend in performing such a task of inference from data is to utilise machine learning algorithms. Moreover, in many applications that it is either required or is pref ...
We derive a framework for sampling online communities based on the mean hitting time of its members, considering that there are multiple graphs associated with the same vertex set V representing the social network. First, we formulate random walk models on ...
The amount of multimedia content is on a constant increase, and people interact with each other and with content on a daily basis through social media systems. The goal of this thesis was to model and understand emerging online communities that revolve aro ...
In today's age of digital multimedia deluge, a clear understanding of the dynamics of online communities is capital. Users have abandoned their role of passive consumers and are now the driving force behind large-scale media repositories, whose dynamics an ...
Web 2.0 streams, like blog postings, microblogging tweets, or RSS feeds from online communities, offer a wealth of latest news about real-world events and societal discussion. From a user’s perspective, it becomes harder and harder to get a decent overview ...
Mechanisms on automatic discovery of macro actions or skills in reinforcement learning methods are mainly focused on subgoal discovery methods. Among the proposed algorithms, those based on graph centrality measures demonstrate a high performance gain. In ...
The École Polytechnique Fédérale de Lausanne is developing a Web 2.0 social software called eLogbook and designed for sustaining interaction, collaboration, and learning in online communities. This chapter describes the 3A model on which eLogbook is based ...
With the increased presence of digital imaging devices there also came an explosion in the amount of multimedia content available online. Users have transformed from passive consumers of media into content creators and have started organizing themselves in ...
Reinforcement Learning (RL) is an approach for training agent's behavior through trial-and-error interactions with a dynamic environment. An important problem of RL is that in large domains an enormous number of decisions are to be made. Hence, instead of ...
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Events occurring in a city, such as car accidents, attacks on the security or activities locations are channelled in the urban network. The goal of this report is to provide a GIS tool able to compute densities and diversities in a network space rather tha ...