1,659 research outputs found

    Summarisation and visualisation of e-Health data repositories

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    At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. We describe a system that we have developed as part of the CLEF project, to perform the task of generating a diverse range of textual and graphical summaries of a patient’s clinical history from a data-encoded model, a chronicle, representing the record of the patient’s medical history. Although the focus of our current work is on cancer patients, the approach we describe is generalisable to a wide range of medical areas

    Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

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    In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

    Capturing Synchronous Collaborative Design Activities: A State-Of-The-Art Technology Review

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    Content vs. context for multimedia semantics: the case of SenseCam image structuring

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    Much of the current work on determining multimedia semantics from multimedia artifacts is based around using either context, or using content. When leveraged thoroughly these can independently provide content description which is used in building content-based applications. However, there are few cases where multimedia semantics are determined based on an integrated analysis of content and context. In this keynote talk we present one such example system in which we use an integrated combination of the two to automatically structure large collections of images taken by a SenseCam, a device from Microsoft Research which passively records a person’s daily activities. This paper describes the post-processing we perform on SenseCam images in order to present a structured, organised visualisation of the highlights of each of the wearer’s days

    Macro-micro approach for mining public sociopolitical opinion from social media

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    During the past decade, we have witnessed the emergence of social media, which has prominence as a means for the general public to exchange opinions towards a broad range of topics. Furthermore, its social and temporal dimensions make it a rich resource for policy makers and organisations to understand public opinion. In this thesis, we present our research in understanding public opinion on Twitter along three dimensions: sentiment, topics and summary. In the first line of our work, we study how to classify public sentiment on Twitter. We focus on the task of multi-target-specific sentiment recognition on Twitter, and propose an approach which utilises the syntactic information from parse-tree in conjunction with the left-right context of the target. We show the state-of-the-art performance on two datasets including a multi-target Twitter corpus on UK elections which we make public available for the research community. Additionally we also conduct two preliminary studies including cross-domain emotion classification on discourse around arts and cultural experiences, and social spam detection to improve the signal-to-noise ratio of our sentiment corpus. Our second line of work focuses on automatic topical clustering of tweets. Our aim is to group tweets into a number of clusters, with each cluster representing a meaningful topic, story, event or a reason behind a particular choice of sentiment. We explore various ways of tackling this challenge and propose a two-stage hierarchical topic modelling system that is efficient and effective in achieving our goal. Lastly, for our third line of work, we study the task of summarising tweets on common topics, with the goal to provide informative summaries for real-world events/stories or explanation underlying the sentiment expressed towards an issue/entity. As most existing tweet summarisation approaches rely on extractive methods, we propose to apply state-of-the-art neural abstractive summarisation model for tweets. We also tackle the challenge of cross-medium supervised summarisation with no target-medium training resources. To the best of our knowledge, there is no existing work on studying neural abstractive summarisation on tweets. In addition, we present a system for providing interactive visualisation of topic-entity sentiments and the corresponding summaries in chronological order. Throughout our work presented in this thesis, we conduct experiments to evaluate and verify the effectiveness of our proposed models, comparing to relevant baseline methods. Most of our evaluations are quantitative, however, we do perform qualitative analyses where it is appropriate. This thesis provides insights and findings that can be used for better understanding public opinion in social media

    Indexing, browsing and searching of digital video

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    Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

    Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

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    This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl
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