Machine Learning and Social Media in Crisis Management: Agility vs Ethics

One of the most used sources of information for fast and flexible crisis information is social media or crowdsourced data, as the information is rapidly disseminated, can reach a large amount of target audience and covers a wide variety of topics. However, the agility that these new methodologies enable comes at a price: ethics and privacy. This paper presents an analysis of the ethical risks and implications of using automated system that learn from social media data to provide intelligence in crisis management. The paper presents a short overview on the use of social media data in crisis management to then highlight ethical implication of machine learning and social media data using an example scenario. In conclusion general mitigation strategies and specific implementation guidelines for the scenario under analysis are presented

Hybrid Search: Effectively Combining Keywords and Semantic Searches

This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontologybased search and keyword-based matching. Hybrid search smoothly copes with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce plc for searching technical documentation about jet engines

Chemical evolution of the bulge of M31: predictions about abundance ratios

We aim at reproducing the chemical evolution of the bulge of M31 by means of a detailed chemical evolution model, including radial gas flows coming from the disk. We study the impact of the initial mass function, the star formation rate and the time scale for bulge formation on the metallicity distribution function of stars. We compute several models of chemical evolution using the metallicity distribution of dwarf stars as an observational constraint for the bulge of M31. Then, by means of the model which best reproduces the metallicity distribution function, we predict the [X/Fe] vs. [Fe/H] relations for several chemical elements (O, Mg, Si, Ca, C, N). Our best model for the bulge of M31 is obtained by means of a robust statistical method and assumes a Salpeter initial mass function, a Schmidt-Kennicutt law for star formation with an exponent k=1.5, an efficiency of star formation of $\sim 15\pm 0.27\, Gyr^{-1}$, and an infall timescale of $\sim 0.10\pm 0.03$Gyr. Our results suggest that the bulge of M31 formed very quickly by means of an intense star formation rate and an initial mass function flatter than in the solar vicinity but similar to that inferred for the Milky Way bulge. The [$\alpha$/Fe] ratios in the stars of the bulge of M31 should be high for most of the [Fe/H] range, as is observed in the Milky Way bulge. These predictions await future data to be proven.Comment: Accepted for publication by MNRA

Roadmapping discussion summary:social media and linked data for emergency response

This paper provides a summary of the Social Media and Linked Data for Emergency Response (SMILE) workshop, co-located with the Extended Semantic Web Conference, at Montpellier, France, 2013. Following paper presentations and question answering sessions, an extensive discussion and roadmapping session was organised which involved the workshop chairs and attendees. Three main topics guided the discussion - challenges, opportunities and showstoppers. In this paper, we present our roadmap towards effectively exploiting social media and semantic web techniques for emergency response and crisis management

Citizens' observatories for situation awareness in flooding

Citizens' observatories are emerging as a means to establish interaction and co-participation between citizens and authorities during both emergencies and the day-to-day management of fundamental resources. In this paper we present a case study in which a model of citizens' observatories is being been translated into practice in the WeSenseIt project. The WeSenseIt citizens' observatory provides a unique way of engaging the public in the decision-making processes associated with water and flood management through a set of new digital technologies. The WeSenseIt citizens' observatory model is being implemented in three case studies based in the UK, the Netherlands and Italy. We describe the findings and our experiences following preliminary evaluations of the technologies and the model of co-participation and describe our future research plans