eStorys: A visual storyboard system supporting back-channel communication for emergencies

This is the post-print version of the final paper published in Journal of Visual Languages & Computing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.In this paper we present a new web mashup system for helping people and professionals to retrieve information about emergencies and disasters. Today, the use of the web during emergencies, is confirmed by the employment of systems like Flickr, Twitter or Facebook as demonstrated in the cases of Hurricane Katrina, the July 7, 2005 London bombings, and the April 16, 2007 shootings at Virginia Polytechnic University. Many pieces of information are currently available on the web that can be useful for emergency purposes and range from messages on forums and blogs to georeferenced photos. We present here a system that, by mixing information available on the web, is able to help both people and emergency professionals in rapidly obtaining data on emergency situations by using multiple web channels. In this paper we introduce a visual system, providing a combination of tools that demonstrated to be effective in such emergency situations, such as spatio/temporal search features, recommendation and filtering tools, and storyboards. We demonstrated the efficacy of our system by means of an analytic evaluation (comparing it with others available on the web), an usability evaluation made by expert users (students adequately trained) and an experimental evaluation with 34 participants.Spanish Ministry of Science and Innovation and Universidad Carlos III de Madrid and Banco Santander

The Music Streaming Sessions Dataset

At the core of many important machine learning problems faced by online streaming services is a need to model how users interact with the content. These problems can often be reduced to a combination of 1) sequentially recommending items to the user, and 2) exploiting the user's interactions with the items as feedback for the machine learning model. Unfortunately, there are no public datasets currently available that enable researchers to explore this topic. In order to spur that research, we release the Music Streaming Sessions Dataset (MSSD), which consists of approximately 150 million listening sessions and associated user actions. Furthermore, we provide audio features and metadata for the approximately 3.7 million unique tracks referred to in the logs. This is the largest collection of such track metadata currently available to the public. This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.Comment: 3 pages, introducing a new large scale datase

Hybrid Profiling in Information Retrieval

Abstract-One of the main challenges in search engine quality of service is how to satisfy the needs and the interests of individual users. This raises the fundamental issue of how to identify and select the information that is relevant to a specific user. This concern over generic provision and the lack of search precision have provided the impetus for the research into Web Search personalisation. In this paper a hybrid user profiling system is proposed -a combination of explicit and implicit user profiles for improving the web search effectiveness in terms of precision and recall. The proposed system is content-based and implements the Vector Space Model. Experimental results, supported by significance tests, indicate that the system offers better precision and recall in comparison to traditional search engines