Traditional information systems for crisis response and management are centralized systems with a rigid hierarchical structure. Here we propose a decentralized system, which allows citizens to play a significant role as information source and/or as helpers during the initial stages of a crisis. In our approach different roles are assigned to citizens. To be able to designate the different roles automatically our system needs to generate appropriate questions. On the basis of information theory and a restricted role ontology we formalized the process of question generation. Three consecutive experiments were conducted with human users to evaluate to what extent the questioning process resulted in appropriate role determination. The result showed that the mental model of human users does not always comply with the formal model underpinning the questions generation process.
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We investigate whether the automatic generation of questions from an ontology leads to a trustworthy determination of a situation. With our Situation Awareness Question Generator (SAQG) we automatically generate questions from an ontology. The experiment shows that people with no previous experience can characterize hectic situations rather fast and trustworthy. When humans are participating as a sensor to gather information it is important to use basic concepts of perception and thought.
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The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.
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