Knowledge Continuous Integration Process (K-CIP)

International audienceSocial semantic web creates read/write spaces where users and smart agents collaborate to produce knowledge readable by humans and machines. An important issue concerns the ontology evolution and evaluation in man-machine collaboration. How to perform a change on ontologies in a social semantic space that currently uses these ontologies through requests ? In this paper, we propose to implement a continuous knowledge integration process named K-CIP. We take advantage of man-machine collaboration to transform feedback of people into tests. This paper presents how K-CIP can be deployed to allow fruitful man-machine collaboration in the context of the WikiTaaable system.Le web sémantique social crée des espaces partagés dans lesquels des utilisateurs et des agents logiciels collabore pour produire de la connaissance utilisable par les humains et les machines. Un problème important est celui de l'évolution et l'évaluation des ontologies dans la collaboration : comment réaliser un changement sur une ontologie dans un espace qui utilise cette ontologie. Dans ce papier, nous proposons de réaliser un processus d'intégration continue de la connaissance nommé K-CIP. Nous tirons profit des retours des utilisateurs dans la collaboration pour construire des tests. Cet article montre comment K-CIP peut être mis en oeuvre pour améliorer la collaboration humain-machine dans le contexte du système WikiTaaable

The power of creative thinking in situations of uncertainties: the almost impossible task of protecting critical infrastructures

A good and scientific analysis starts with a closer look at the conceptualisation at hand. The definition of CIP is not easy because of its wide range. This paper examines infrastructures that are critical and need protection. Each word entails a specific connotation and is characterized by several components

No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models

For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which typically predict graphemes or words, simplify the recognition process since they remove the need for a separate expert-curated pronunciation lexicon to map from phoneme-based units to words. However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units. In this work, we conduct detailed experiments which are aimed at quantifying the value of phoneme-based pronunciation lexica in the context of end-to-end models. We examine phoneme-based end-to-end models, which are contrasted against grapheme-based ones on a large vocabulary English Voice-search task, where we find that graphemes do indeed outperform phonemes. We also compare grapheme and phoneme-based approaches on a multi-dialect English task, which once again confirm the superiority of graphemes, greatly simplifying the system for recognizing multiple dialects

Participatory varietal selection of potato using the mother & baby trial design: A gender-responsive trainer’s guide.

This guide aims to provide step-by-step guidance on facilitating and documenting the PVS dynamics using the MBT design to select, and eventually release, potato varieties preferred by end-users that suit male and female farmers ’different needs, diverse agro-systems, and management practices, as well as traders ’and consumers’ preferences