What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data
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We tackle the problem of improving the relevance of automatically selected tags in large-scale ontology-based information systems. Contrary to traditional settings where tags can be chosen arbitrarily, we focus on the problem of recommending tags (e.g., co ...
As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuo ...
There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, ...
In this thesis, we address the analysis of activities from long term data logs with an emphasis on video recordings. Starting from simple words from video, we progressively build methods to infer higher level scene semantics. The main strategies used to ac ...
In this work we discover the daily location-driven routines which are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines which characterize both individual and group behaviors ...
Statistics of spatial extremes is developing very rapidly, owing to the demands of applications in the environmental sciences and the insurance and risk industries. This entry sketches the main ideas, based on classical extreme-value statistics. The two ma ...
Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usag ...
We describe a failure of standard extremal models to account for a catastrophic rainfall event in the coastal regions of Venezuela on 14-16 December 1999, due both to inaccurate tail modelling and to an inadequate treatment of clusters of rare events. We i ...
This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) a ...
Tasks that rely on semantic content of documents, notably Information Retrieval and Document Classification, can benefit from a good account of document context, i.e. the semantic association between documents. To this effect, the scheme of latent semantic ...