This paper introduces an evaluated approach to the automatic generation of video narratives from user generated content gathered in a shared repository. In the context of social events, end-users record video material with their personal cameras and upload the content to a common repository. Video narrative techniques, implemented using Narrative Structure Language (NSL) and ShapeShifting Media [Ursu, 2008a], are employed to automatically generate movies recounting the event. Such movies are personalized according to the preferences expressed by each individual end-user, for each individual viewing. This paper describes our prototype narrative system, MyVideos, deployed as a web application, and reports on its evaluation for one specific use case: assembling stories of a school concert by parents, relatives and friends. The evaluations carried out through focus groups, interviews and field trials, in the Netherlands and UK, provided validating results and further insights into this approach.
This paper introduces a multimedia document model that can structure community comments about media. In particular, we describe a set of temporal transformations for multimedia documents that allow end-users to create and share personalized timed-text comments on third party videos. The benefit over current approaches lays in the usage of a rich captioning format that is not embedded into a specific video encoding format. Using as example a Web-based video annotation tool, this paper describes the possibility of merging video clips from different video providers into a logical unit to be captioned, and tailoring the annotations to specific friends or family members. In addition, the described transformations allow for selective viewing and navigation through temporal links, based on end-users' comments. We also report on a predictive timing model for synchronizing unstructured comments with specific events within a video(s). The contributions described in this paper bring significant implications to be considered in the analysis of rich media social networking sites and the design of next generation video annotation tools.
Over the recent decades researchers in academia and central banks have developed early warning systems (EWS) designed to warn policy makers of potential future economic and financial crises. These EWS are based on diverse approaches and empirical models. In this paper we compare the performance of nine distinct models for predicting banking crises resulting from the work of the Macroprudential Research Network (MaRs) initiated by the European System of Central Banks. In order to ensure comparability, all models use the same database of crises created by MaRs and comparable sets of potential early warning indicators. We evaluate the models' relative usefulness by comparing the ratios of false alarms and missed crises and discuss implications for pratical use and future research. We find that multivariate models, in their many appearances, have great potential added value over simple signalling models. One of the main policy recommendations coming from this exercise is that policy makers can benefit from taking a broad methodological approach when they develop models to set macro-prudential instruments.The obligatory copyright note: We certify that we have the right to deposit the contribution with MPRA.
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