Experiments in Diversifying Flickr Result Sets

The 2013 MediaEval Retrieving Diverse Social Images Task looked to tackling the problem of search result diversification of Flickr results sets formed from queries about geographic places and landmarks. In this paper we describe our approach of using a min-max similarity diversifier coupled with pre-filters and a reranker. We also demonstrate a number of novel features for measuring similarity to use in the diversification step

Identifying the Geographic Location of an Image with a Multimodal Probability Density Function

There is a wide array of online photographic content that is not geotagged. Algorithms for efficient and accurate geographical estimation of an image are needed to geolocate these photos. This paper presents a general model for using both textual metadata and visual features of photos to automatically place them on a world map

Optimization of delays experienced by packets due to ACLs within a domain

The infrastructure of large networks is broken down into areas that have a common security policy called a domain. Security within a domain is commonly implemented at all nodes however this has a negative effect on performance since it introduces a delay associated with packet filtering. Recommended techniques for network design imply that every packet should be checked at the first possible ingress points of the network. When access control lists (ACL's) are used within a router for this purpose then there can be a significant overhead associated with this process. The purpose of this paper is to consider the effect of delays when using router operating systems offering different levels of functionality. It considers factors which contribute to the delay particularly due to ACL. Using theoretical principles modified by practical calculation a model is created for packet delay for all nodes across a given path in a domain

Making asset investment decisions for wastewater systems that include sustainability

Effective integrated water management is a key component of the World Water Vision and the way in which aspirations for water equity may be realized. Part of the vision includes the promotion of sustainability of water systems and full accountability for their interaction with other urban systems. One major problem is that “sustainability” remains an elusive concept, although those involved with the provision of urban wastewater systems now recognize that decisions involving asset investment should use the “triple bottom line” approach to society, the economy, and the environment. The Sustainable Water Industry Asset Resource Decisions project has devised a flexible and adaptable framework of decision support processes that can be used to include the principles of sustainability more effectively. Decision mapping conducted at the outset of the project has shown that only a narrow range of criteria currently influence the outcome of asset investment decisions. This paper addresses the concepts of sustainability assessment and presents two case studies that illustrate how multicriteria decision support systems can enhance the assessment of the relative sustainability of a range of options when decisions are being made about wastewater asset investment

Social Event Detection via sparse multi-modal feature selection and incremental density based clustering

Combining items from social media streams, such as Flickr photos and Twitter tweets, into meaningful groups can help users contextu- alise and effectively consume the torrents of information now made available on the social web. This task is made challenging due to the scale of the streams and the inherently multimodal nature of the information to be contextualised. We present a methodology which approaches social event detection as a multi-modal clustering task. We address the various challenges of this task: the selection of the features used to compare items to one another; the construction of a single sparse affinity matrix; combining the features; relative importance of features; and clustering techniques which produce meaningful item groups whilst scaling to cluster large numbers of items. In our best tested configuration we achieve an F1 score of 0.94, showing that a good compromise between precision and recall of clusters can be achieved using our technique