Are tiled display walls needed for astronomy?

Clustering commodity displays into a Tiled Display Wall (TDW) provides a cost-effective way to create an extremely high resolution display, capable of approaching the image sizes now gen- erated by modern astronomical instruments. Astronomers face the challenge of inspecting single large images, many similar images simultaneously, and heterogeneous but related content. Many research institutions have constructed TDWs on the basis that they will improve the scientific outcomes of astronomical imagery. We test this concept by presenting sample images to astronomers and non- astronomers using a standard desktop display (SDD) and a TDW. These samples include standard English words, wide field galaxy surveys and nebulae mosaics from the Hubble telescope. These experiments show that TDWs provide a better environment for searching for small targets in large images than SDDs. It also shows that astronomers tend to be better at searching images for targets than non-astronomers, both groups are generally better when employing physical navigation as opposed to virtual navigation, and that the combination of two non-astronomers using a TDW rivals the experience of a single astronomer. However, there is also a large distribution in aptitude amongst the participants and the nature of the content also plays a significant role is success.Comment: 19 pages, 15 figures, accepted for publication in PASA (Publications of the Astronomical Society of Australia

Techniques for effective and efficient fire detection from social media images

Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFDnR), which combines feature extractor and evaluation functions to support instance-based learning, (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -- the FlickrFire dataset, and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFDnR was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.Comment: 12 pages, Proceedings of the International Conference on Enterprise Information Systems. Specifically: Marcos Bedo, Gustavo Blanco, Willian Oliveira, Mirela Cazzolato, Alceu Costa, Jose Rodrigues, Agma Traina, Caetano Traina, 2015, Techniques for effective and efficient fire detection from social media images, ICEIS, 34-4

Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the information required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier capable of combining them. In this paper we explore several methods of creating such a classifier, including early, late, hybrid fusion and representation learning using multimodal graphs. We evaluate performance on a real world dataset obtained from a live citizen reporting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demonstrate that our hybrid combination of early and late fusion with multimodal embeddings performs best in classification of urban micro-events

Capability in the digital: institutional media management and its dis/contents

This paper explores how social media spaces are occupied, utilized and negotiated by the British Military in relation to the Ministry of Defence’s concerns and conceptualizations of risk. It draws on data from the DUN Project to investigate the content and form of social media about defence through the lens of ‘capability’, a term that captures and describes the meaning behind multiple representations of the military institution. But ‘capability’ is also a term that we hijack and extend here, not only in relation to the dominant presence of ‘capability’ as a representational trope and the extent to which it is revealing of a particular management of social media spaces, but also in relation to what our research reveals for the wider digital media landscape and ‘capable’ digital methods. What emerges from our analysis is the existence of powerful, successful and critically long-standing media and reputation management strategies occurring within the techno-economic online structures where the exercising of ‘control’ over the individual – as opposed to the technology – is highly effective. These findings raise critical questions regarding the extent to which ‘control’ and management of social media – both within and beyond the defence sector – may be determined as much by cultural, social, institutional and political influence and infrastructure as the technological economies. At a key moment in social media analysis, then, when attention is turning to the affordances, criticisms and possibilities of data, our research is a pertinent reminder that we should not forget the active management of content that is being similarly, if not equally, effective