Optimising ECOC matrices in multi-class classification problems

Error Correcting Output Coding (ECOC) is a multi-class classiffication technique in which multiple binary classiffiers are trained according to a preset code matrix, such that each one learns a separate dichotomy of the classes. While ECOC is one of the best solutions to multi-class problems, it is suboptimal since the code matrix and the base classiffiers are not learned simultaneously. In this thesis, we present three different algorithms that iteratively updates the ECOC code matrix to improve the performance of the ensemble by reducing the decoupling. Firstly, we applied the previously developed FlipECOC+ update algorithm. Second method is applying simulated annealing method on updating ECOC matrix by flipping proposed entries according to ascending order. Last method is applying beam search to find updated ECOC matrix which has highest validation accuracy. We applied all three algorithms on UCI (University of California Irvine) data sets. Beam search algorithm gives the best result on UCI data sets. All of the proposed update algorithms does not involve further training of the classiffiers and can be applied to any ECOC ensemble

Aide ambiante à la personne par apprentissage profond

Ambient assisted living aims to support the aging population. This is particularly the case with smart homes, equipped with multiple connected sensors, which enables to extend home care for the elderly. The manuscript begins by introducing the general problem of smart homes, after presenting further the three sub-themes that are the subject of the thesis, namely the activity recognition, privacy and dialogue systems.Activity recognition is the process of determining the day-to-day activities of a person or a group of people from the (raw) sensor data that the home is equipped with. An example of this is the detection of a person's fall. A smart home is typically based on the Internet of Things (IoT). Many data are produced, which may contain private or sensitive information. Some of this data must be shared externally, which may pose privacy issues. Finally, a natural way of communication for the user is to use the dialogue to interact with the smart home via dialogue manager.This thesis proposes contributions on these three sides, most of them based on deep learning.L'aide ambiante à la personne (ambiant assisted living) a pour objectif d'accompagner le vieillissement de la population. Cela s'instancie notamment par les maisons intelligentes (smart homes), équipées de multiples capteurs connectés, dont un des objectifs est de prolonger le maintien à domicile des personnes âgées. Le manuscrit s'attache d'abord à introduire la problématique générale des maisons intelligentes, avant de présenter plus avant les trois sous-thématiques qui font plus particulièrement l'objet de la thèse, à savoir la reconnaissance d'activités, la confidentialité et les systèmes de dialogue.La reconnaissance d'activités consiste à déterminer les activités courantes d'une personne ou d'un groupe de personnes, à partir des données (brutes) des capteurs dont est équipée la maison. On peut citer comme exemple la détection de la chute d'une personne. Une maison intelligent repose typiquement sur l'internet des objets (Internet of Things, ou IoT). De nombreuses données sont produites, pouvant contenir des informations privées ou sensibles. Une partie de ces données doit être partagée avec l'extérieur, ce qui peut poser des problèmes de confidentialité. Enfin, pour interragir avec la maison intelligente, un moyen naturel pour l'utilisateur est d'utiliser le dialogue, sujet traité par les systèmes de dialogue.Ce travail de thèse propose des contributions sur ces trois versants, la plupart basées sur l'apprentissage profond

Fluorimetric detection of boron by azomethine-H in micellar solution and sol-gel

Mixtures of boron and azomethine-H in solution result in slow complexation. Addition of sodium dodecyl sulfate (SDS), polyethylene glycol dodecyl ether (Brij-35), 4-(1,1,3,3-tetramethylbutyl)phenyl-polyethylene glycol (TritonX-100), and cetyltrimethyl ammonium bromide (CTAB) result in considerable decrease in complexation time and enhancement in signal of peak in solution and also sol-gel. The fluorescence of the complex is monitored at an emission wavelength of 486 nm with excitation at 416 nm. The presence of 1 x 10(-3) mol L-1 SDS decreased the complexation time up to 10 min in solution and 20 min in sol-gel for above 0.25 mu g B mL(-1) and 30 min in solution and 35 min in sol-gel for below 0.25 mu g B mL(-1). However, the photostability did not change by adding micelle in both media. The proposed method shows a linear response toward boron in the concentration range of 0.05-10 mu g mL(-1) and is selective for boron over a large number of electrolytes and cations. The detection limit was 7 mu g L-1. This method has been used for the detection of boron in environmental water samples and fruit juices with satisfactory results

Reconstruct & Crush Network

International audienceThis article introduces an energy-based model that is adversarial regarding data: it minimizes the energy for a given data distribution (the positive samples) while maximizing the energy for another given data distribution (the negative or unlabeled samples). The model is especially instantiated with autoencoders where the energy, represented by the reconstruction error, provides a general distance measure for unknown data. The resulting neural network thus learns to reconstruct data from the first distribution while crushing data from the second distribution. This solution can handle different problems such as Positive and Unlabeled (PU) learning or covariate shift, especially with imbalanced data. Using autoencoders allows handling a large variety of data, such as images, text or even dialogues. Our experiments show the flexibility of the proposed approach in dealing with different types of data in different settings: images with CIFAR-10 and CIFAR-100 (not-in-training setting), text with Amazon reviews (PU learning) and dialogues with Facebook bAbI (next response classification and dialogue completion)

Modified Actor-Critics

International audienc

A fluorescent chemical sensor for ethanol determination in alcoholic beverages

A sensor for ethanol is described that is based on the fluorescent probe 5,10,15,20-tetraphenyl porphyrin (TPP). Response is based on the quenching of the fluorescence of TPP by ethanol as a result of electrostatic attraction. The sensor linearly responds to ethanol in the concentration range from 1 to 75 vol.% and was applied to the determination of ethanol in various kinds of wines and whisky

Dialogue Systems for Intelligent Human Computer Interactions

International audienceThe most fundamental communication mechanism for interaction is dialogues involving speech, gesture, semantic and pragmatic knowledge. Various researches on dialogue management have been conducted focusing on standardized model for goal oriented applications using machine learning and deep learning models. The paper presents the overview on existing methods for dialogue manager training; their advantages and limitations. Furthermore, a new image-based method is used in Facebook bAbI Task 1 dataset in Out Of Vocabulary setting. The results show that using dialogue as an image performs well and helps dialogue manager in expanding out of vocabulary dialogue tasks in comparison to Memory Networks

BeamECOC: A local search for the optimization of the ECOC matrix

Error Correcting Output Coding (ECOC) is a multi- class classification technique in which multiple binary classifiers are trained according to a preset code matrix such that each one learns a separate dichotomy of the classes. While ECOC is one of the best solutions for multi-class problems, one issue which makes it suboptimal is that the training of the base classifiers is done independently of the generation of the code matrix. In this paper, we propose to modify a given ECOC matrix to improve its performance by reducing this decoupling. The proposed algorithm uses beam search to iteratively modify the original matrix, using validation accuracy as a guide. It does not involve further training of the classifiers and can be applied to any ECOC matrix. We evaluate the accuracy of the proposed algorithm (BeamE- COC) using 10-fold cross-validation experiments on 6 UCI datasets, using random code matrices of different sizes, and base classifiers of different strengths. Compared to the random ECOC approach, BeamECOC increases the average cross-validation accuracy in 83 : 3% of the experimental settings involving all datasets, and gives better results than the state-of-the-art in 75% of the scenarios. By employing BeamECOC, it is also possible to reduce the number of columns of a random matrix down to 13% and still obtain comparable or even better results at times

Human Annotated Dialogues Dataset for Natural Conversational Agents

International audienceConversational agents are gaining huge popularity in industrial applications such as digital assistants, chatbots, and particularly systems for natural language understanding (NLU). However, a major drawback is the unavailability of a common metric to evaluate the replies against human judgement for conversational agents. In this paper, we develop a benchmark dataset with human annotations and diverse replies that can be used to develop such metric for conversational agents. The paper introduces a high-quality human annotated movie dialogue dataset, HUMOD, that is developed from the Cornell movie dialogues dataset. This new dataset comprises 28,500 human responses from 9500 multi-turn dialogue history-reply pairs. Human responses include: (i) ratings of the dialogue reply in relevance to the dialogue history; and (ii) unique dialogue replies for each dialogue history from the users. Such unique dialogue replies enable researchers in evaluating their models against six unique human responses for each given history. Detailed analysis on how dialogues are structured and human perception on dialogue score in comparison with existing models are also presented