We present both a theoretical model and its related web based framework for collaborative scientific to technology using “DiscInNet process”. This is a system of scientific research diffusion specifying different phases of progress (published, shaping, archived, etc.) therefore allowing to share the ongoing research results. The process shows how agents of different levels, from human to automata, may fruitfully interact to develop an interdisciplinary multiscale vision of science, its trends and technological outcomes.
Communications
The presentation of the communications of the CHIST-ERA Conference 2012 (keynote and short talks, posters) will be continuously updated in the course of June.

Dr. Olga Kozlova is research coordinator at DiscInNet Labs.
Poster
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Dr. Philippe Lenca is lecturer at the French school of engineering Telecom Bretagne and researcher in the Information and Communication Science and Technology laboratory where is in charge of the Decision Aid and Knowledge Discovery (DECIDE) group.
In a broad sense, KDD is defined as a non-trivial, interactive and iterative process, where the users seek to identify valid, novel, potentially useful, and ultimately understandable patterns in data. However in practice KDD must be considered as a process of contextualization: exact definitions of all concepts are required. In particular the notion of interestingness (validity, novelty, utility, understandability, actionability, etc.) is a broad concept. Main issues, current investigations and solutions about interestingness will be discussed.
Poster
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Dr. Przemek Lenkiewicz is postdoc researcher in the Language Archive Group of the Max Planck Institute for Psycholinguistics, the Netherlands.
How automatic annotation of heterogeneous multimedia documents may help the understanding of latent knowledge and the formation of a coherent set of beliefs, based on the audiovisual corpus of Max Planck Institute. Large audiovisual repositories from Humanities research still pose great challenges for automatic annotation. A promising approach combines three elements: 1) simple and effective detectors (sounds, speech, objects, gestures...), 2) interactive tools to collect significance assessment from human operators, and 3) automated learning engines consistent with human feedback.
Poster
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