Learning Pretopological Spaces for Lexical Taxonomy Acquisition

International audienceIn this paper, we propose a new methodology for semi-supervised acquisition of lexical taxonomies from a list of existing terms. Our approach is based on the theory of pretopology that offers a powerful formalism to model semantic relations and transform a list of terms into a structured term space by combining different discriminant criteria. In order to learn a parameterized pretopological space, we define the Learning Pretopological Spaces strategy based on genetic algorithms. The rare but accurate pieces of knowledge given by an expert (semi-supervision) or automatically extracted with existing linguistic patterns (auto-supervision) are used to parameterize the different features defining the pretopological term space. Then, a structuring algorithm is used to transform the pretopological space into a lexical taxonomy, i.e. a direct acyclic graph. Results over three standard datasets (two from WordNet and one from UMLS) evidence improved performances against existing associative and pattern-based state-of-the-art approaches

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Explaining deep neural networks through knowledge extraction and graph analysis

Explainable Artificial Intelligence (XAI) has recently become an active research field due to the need for transparency and accountability when deploying AI models for high-stake decision making. Despite the success of Deep Neural Networks (DNNs), understanding their decision processes is still a known challenge. The research direction presented in this thesis stems from the idea that combining knowledge with deep representations can be the key to more transparent decision making. Specifically, we have focused on Computer Vision tasks and Convolutional Neural Networks (CNNs) and we have proposed a graph representation, called co-activation graph, that serves as an intermediate representation between knowledge encoded within a CNN and the semantics contained in external knowledge bases. Given a trained CNN, we first show how a co-activation graph can be created and exploited to generate global insights for the model’s inner-workings. Then, we propose a taxonomy extraction method that captures how symbolic class concepts and their hypernyms in a given domain are hierarchically organised in the model’s subsymbolic representation. We then illustrate how background knowledge can be connected to the graph in order to generate textual local factual and counterfactual explanations. Our results indicate that graph analysis approaches applied to co-activation graphs can reveal important insights into how CNNs work and enable both global and local semantic explanations. Despite focusing on CNN architectures, we believe that our approach can be adapted to other architectures which would make it possible to apply the same methodology in other domains such as Natural Language Processing. At the end of the thesis we will discuss interesting research directions that are being investigated in the area of using knowledge graphs and graph analysis for explainability of deep learning models, and we outline opportunities for the development of this research are

Feature Learning for RGB-D Data

RGB-D data has turned out to be a very useful representation for solving fundamental computer vision problems. It takes the advantages of the color images that provide appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. RGB-D image/video can facilitate a wide range of application areas, such as computer vision, robotics, construction and medical imaging. Furthermore, how to fuse RGB information and depth information is still a problem in computer vision. It is not enough to simply concatenate RGB data and depth data together. A new fusion method could better fuse RGB images and depth images. It still needs more powerful algorithms on this. In this thesis, to explore more advantages of RGB-D data, we use some popular RGB-D datasets for deep feature learning algorithms evaluation, hyper-parameter optimization, local multi-modal feature learning, RGB-D data fusion and recognizing RGB information from RGB-D images: i)With the success of Deep Neural Network in computer vision, deep features from fused RGB-D data can be proved to gain better results than RGB data only. However, different deep learning algorithms show different performance on different RGB-D datasets. Through large-scale experiments to comprehensively evaluate the performance of deep feature learning models for RGB-D image/ video classification, we obtain the conclusion that RGB-D fusion methods using CNNs always outperform other selected methods (DBNs, SDAE and LSTM). On the other side, since LSTM can learn from experience to classify, process and predict time series, it achieved better performances than DBN and SDAE in video classification tasks. ii) Hyper-parameter optimization can help researchers quickly choose an initial set of hyper-parameters for a new coming classification task, thus reducing the number of trials in terms of hyper-parameter space. We present a simple and efficient framework for improving the efficiency and accuracy of hyper-parameter optimization by considering the classification complexity of a particular dataset. We verify this framework on three real-world RGB-D datasets. After the analysis of experiments, we confirm that our framework can provide deeper insights into the relationship between dataset classification tasks and hyperparameters optimization, thus quickly choosing an accurate initial set of hyper-parameters for a new coming classification task. iii) We propose a new Convolutional Neural Networks (CNNs)-based local multi-modal feature learning framework for RGB-D scene classification. This method can effectively capture much of the local structure from the RGB-D scene images and automatically learn a fusion strategy for the object-level recognition step instead of simply training a classifier on top of features extracted from both modalities. Experiments are conducted on two popular datasets to thoroughly test the performance of our method, which show that our method with local multi-modal CNNs greatly outperforms state-of-the-art approaches. Our method has the potential to improve RGB-D scene understanding. Some extended evaluation shows that CNNs trained using a scene-centric dataset is able to achieve an improvement on scene benchmarks compared to a network trained using an object-centric dataset. iv) We propose a novel method for RGB-D data fusion. We project raw RGB-D data into a complex space and then jointly extract features from the fused RGB-D images. Besides three observations about the fusion methods, the experimental results also show that our method achieves competing performance against the classical SIFT. v) We propose a novel method called adaptive Visual-Depth Embedding (aVDE) which learns the compact shared latent space between two representations of labeled RGB and depth modalities in the source domain first. Then the shared latent space can help the transfer of the depth information to the unlabeled target dataset. At last, aVDE matches features and reweights instances jointly across the shared latent space and the projected target domain for an adaptive classifier. This method can utilize the additional depth information in the source domain and simultaneously reduce the domain mismatch between the source and target domains. On two real-world image datasets, the experimental results illustrate that the proposed method significantly outperforms the state-of-the-art methods

An analysis of target recipient groups for monovalent 2009 pandemic influenza vaccine and trivalent seasonal influenza vaccines in 2009-10 and 2010-11

Poster Presentation: SPA5 - How to Evaluate Vaccine Effectiveness and Efficacy?: abstract no. A513PINTRODUCTION: Vaccination is generally considered to be the best primary prevention measure against influenza virus infection. Many countries encourage specific target groups of people to undertake vaccination, often with financial subsidies or a list of priority. To understand differential patterns of national target groups for influenza vaccination before, during and after the 2009 influenza pandemic, we reviewed and identified changes in national target groups for trivalent seasonal influenza and the monovalent 2009 pandemic influenza vaccines dur...postprin

Efficacy of live attenuated seasonal and pandemic influenza vaccine in school-age children: a randomized controlled trial

Poster Presentation: SPA5 - How to Evaluate Vaccine Effectiveness and Efficacy?: abstract no. A508PBACKGROUND: A novel pandemic influenza A(H1N1) virus emerged in North America in early 2009 and rapidly spread worldwide. Monovalent pH1N1 vaccines were licensed later in 2009 based on preliminary studies demonstrating their immunogenicity and safety. In this study we report the efficacy of live attenuated monovalent pH1N1 vacc...postprin

A modular genetic programming system

Genetic Programming (GP) is an evolutionary algorithm for the automatic discovery of symbolic expressions, e.g. computer programs or mathematical formulae, that encode solutions to a user-defined task. Recent advances in GP systems and computer performance made it possible to successfully apply this algorithm to real-world applications. This work offers three main contributions to the state-of-the art in GP systems: (I) The documentation of RGP, a state-of-the art GP software implemented as an extension package to the popular R environment for statistical computation and graphics. GP and RPG are introduced both formally and with a series of tutorial examples. As R itself, RGP is available under an open source license. (II) A comprehensive empirical analysis of modern GP heuristics based on the methodology of Sequential Parameter Optimization. The effects and interactions of the most important GP algorithm parameters are analyzed and recommendations for good parameter settings are given. (III) Two extensive case studies based on real-world industrial applications. The first application involves process control models in steel production, while the second is about meta-model-based optimization of cyclone dust separators. A comparison with traditional and modern regression methods reveals that GP offers equal or superior performance in both applications, with the additional benefit of understandable and easy to deploy models. Main motivation of this work is the advancement of GP in real-world application areas. The focus lies on a subset of application areas that are known to be practical for GP, first of all symbolic regression and classification. It has been written with practitioners from academia and industry in mind