SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies

Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap

Visualizing collaborations and online social interactions at scientific conferences for scholarly networking

The various ways of interacting with social media, web collaboration tools, co-authorship and citation networks for scientific and research purposes remain distinct. In this paper, we propose a solution to align such information. We particularly developed an exploratory visualization of research networks. The result is a scholar centered, multi-perspective view of conferences and people based on their collaborations and online interactions. We measured the relevance and user acceptance of this type of interactive visualization. Preliminary results indicate a high precision both for recognized people and conferences. The majority in a group of test-users responded positively to a set of statements about the acceptance

Life Monza: project description and actions’ updating

The introduction of Low Emission Zones, urban areas subject to road traffic restrictions in order to ensure compliance with the air pollutants limit values set by the European Directive on ambient air quality (2008/50/EC), is a common and well-established action in the administrative government of cities. The impacts on air quality improvement are widely analysed, whereas the effects and benefits concerning the noise have not been addressed in a comprehensive manner. As a consequence, the definition, the criteria for the analysis and the management methods of a Noise Low Emission Zone are not clearly expressed and shared yet. The LIFE MONZA project (Methodologies fOr Noise low emission Zones introduction And management - LIFE15 ENV/IT/000586) addresses these issues. The first objective of the project, co-funded by the European Commission, is to introduce an easy-replicable method for the identification and the management of the Noise Low Emission Zone, an urban area subject to traffic restrictions, whose impacts and benefits regarding noise issues will be analyzed and tested in the pilot area of the city of Monza, located in Northern Italy. Background conditions, structure, objectives of the project and actions’ progress will be discussed in this article

MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-based agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG, on the other hand, achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc

Control What You Include! Server-Side Protection against Third Party Web Tracking

Third party tracking is the practice by which third parties recognize users accross different websites as they browse the web. Recent studies show that 90% of websites contain third party content that is tracking its users across the web. Website developers often need to include third party content in order to provide basic functionality. However, when a developer includes a third party content, she cannot know whether the third party contains tracking mechanisms. If a website developer wants to protect her users from being tracked, the only solution is to exclude any third-party content, thus trading functionality for privacy. We describe and implement a privacy-preserving web architecture that gives website developers a control over third party tracking: developers are able to include functionally useful third party content, the same time ensuring that the end users are not tracked by the third parties

Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search