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IntuiScript is an innovative project aiming at the development of a digital workbook providing feedback during the handwriting learning process for children from three to seven years old. In this context, the paper presents a method to analyse handwriting quality that responds to the expectations of the IntuiScript educational scenario: on-line and real time feedback for children, an automatic detection of children mistakes guiding the pedagogical progression, and a precise analysis of children writing saved to help teacher to understand children writing skills. The presented method introduces a multi-criteria architecture to analyse handwriting quality based on three different aspects: shape, order and direction. The validation of the proposed approach is done on a realistic dataset collected in preschools and primary schools with 952 children. Results show a positive feedback of children and teachers about the use of tactile digital devices, and a significant improvement of the performances of the multi-criteria architecture compared to the previous analyser. The ground truth has been annotated by experts with different levels of confidence. Specific evaluation metrics are introduced to deal with confidence annotations.

This paper presents an open tool for standardizing the evaluation process of the layout analysis task of document images at pixel level. We introduce a new evaluation tool that is both available as a standalone Java application and as a RESTful web service. This evaluation tool is free and open-source in order to be a common tool that anyone can use and contribute to. It aims at providing as many metrics as possible to investigate layout analysis predictions, and also provides an easy way of visualizing the results. This tool evaluates document segmentation at pixel level, and supports multi-labeled pixel ground truth. Finally, this tool has been successfully used for the ICDAR 2017 competition on Layout Analysis for Challenging Medieval Manuscripts.

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.

Part 7: Gesture-Based User Interface Design and Interaction IIInternational audienceThis paper presents a new design and evaluation of customizable gesture commands on pen-based devices. Our objective is to help users during the definition of gestures by detecting confusion among gestures. We also help the memorization gestures with the guide of a new type of menu “Customizable Gesture Menus”. These menus are associated with an evolving gesture recognition engine that learns incrementally, starting from few data samples. Our research focuses on making user and recognition system learn at the same time, hence the term “cross-learning”. Three experimentations are presented in details in this paper to support these ideas

Abstract-This paper tackles the problem of decremental learning of an evolving classification system. We study the use of decremental learning to improve performance of evolving recognizers in non-stationary scenarios. Our on-line recognizer is based on an evolving fuzzy inference system. In this paper, we propose a new strategy to introduce decremental learning, with the use of a sliding window, in the optimization of fuzzy rules conclusions. This approach is based on a downdating technique of least squares solutions for unlearning old data. This technique is evaluated on handwritten gesture recognition tasks. In particular, it is shown that this downdating techniques allow to adapt to concept drifts and that we face a precision reactiveness trade-off. It is also demonstrated that decremental learning is necessary to maintain the system learning capacity over time, making decremental learning essential for the lifetime use of an evolving classification system.

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This paper presents a new method to help users defining personalized gesture commands (on pen-based devices) that maximize recognition performance from the classifier. The use of gesture commands give rise to a cross-learning situation where the user has to learn and memorize the command gestures and the classifier has to learn and recognize drawn gestures. The classification task associated with the use of customized gesture commands is complex because the classifier only has very few samples per class to start learning from. We thus need an evolving recognition system that can start from scratch or very few data samples and that will learn incrementally to achieve good performance after some using time. Our objective is to make the user aware of the recognizer difficulties during the definition of commands, by detecting confusion among gesture classes, in order to help him define a gesture set that yield good recognition performance from the beginning. To detect confusing classes we apply confusion reject principles to our evolving recognizer, which is based on a first order fuzzy inference system. A realistic experiment has been made on 55 persons to validate our confusion detection technique, and it shows that our method leads to a significant improvement of the classifier recognition performance.