Computing Latent Taxonomies from Patients'Spontaneous Self-Disclosure to Form Compatible Support Groups

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Design of the Narrator System: processing, storing, and retrieving medical narrative data

Contains fulltext : 54781.pdf (publisher's version ) (Open Access)In the context of patients communicating about their disease, there are several channels along which this can be done. Most of these channels do not take the patient as primary input, but provide authoritative information. The Narrator system supplies patients with information extracted from personal stories in plain text format called "narratives". These will be processed and stored using techniques from both Information Retrieval and Natural Language Processing. As such, the system will be set up as a toolbox implementing different approaches while a Service Oriented Architecture provides the framework for integration. In this paper such approaches are described together with efforts to combine them within a suitable architecture. Furthermore, some of the important implementation details are discussed. As a starting point for the system, experiments have been carried out with initial narratives, the results of which are discussed

Finding relevant passages using noun-noun compounds: Coherence vs. proximity

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Analysis of the Impact of Data Granularity on Privacy for the Smart Grid

The upgrade of the electricity network to the "smart grid" has been intensified in the last years. The new automated devices being deployed gather large quantities of data that offer promises of a more resilient grid but also raise privacy concerns among customers and energy distributors. In this paper, we focus on the energy consumption traces that smart meters generate and especially on the risk of being able to identify individual customers given a large dataset of these traces. This is a question raised in the related literature and an important privacy research topic. We present an overview of the current research regarding privacy in the Advanced Metering Infrastructure. We make a formalization of the problem of de-anonymization by matching low-frequency and high-frequency smart metering datasets and we also build a threat model related to this problem. Finally, we investigate the characteristics of these datasets in order to make them more resilient to the de-anonymization process. Our methodology can be used by electricity companies to better understand the properties of their smart metering datasets and the conditions under which such datasets can be released to third parties