Formação do significado perceptual das palavras através de interacção

Doutoramento em Engenharia InformáticaThis thesis addresses the problem of word learning in computational agents. The motivation behind this work lies in the need to support language-based communication between service robots and their human users, as well as grounded reasoning using symbols relevant for the assigned tasks. The research focuses on the problem of grounding human vocabulary in robotic agent’s sensori-motor perception. Words have to be grounded in bodily experiences, which emphasizes the role of appropriate embodiments. On the other hand, language is a cultural product created and acquired through social interactions. This emphasizes the role of society as a source of linguistic input. Taking these aspects into account, an experimental scenario is set up where a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. The agent grounds the names of these objects in visual perception. Word learning is an open-ended problem. Therefore, the learning architecture of the agent will have to be able to acquire words and categories in an openended manner. In this work, four learning architectures were designed that can be used by robotic agents for long-term and open-ended word and category acquisition. The learning methods used in these architectures are designed for incrementally scaling-up to larger sets of words and categories. A novel experimental evaluation methodology, that takes into account the openended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. The results indicate that all approaches were able to incrementally acquire new words and categories. Although some of the approaches could not scale-up to larger vocabularies, one approach was shown to learn up to 293 categories, with potential for learning many more.Esta tese aborda o problema da aprendizagem de palavras em agentes computacionais. A motivação por trás deste trabalho reside na necessidade de suportar a comunicação baseada em linguagem entre os robôs de serviço e os seus utilizadores humanos, bem como suportar o raciocínio baseado em símbolos que sejam relevantes no contexto das tarefas atribuídas e cujo significado seja definido com base na experiência perceptiva. Mais especificamente, o foco da investigação é o problema de estabelecer o significado das palavras na percepção do robô através da interacção homemrobô. A definição do significado das palavras com base em experiências perceptuais e perceptuo-motoras enfatiza o papel da configuração física e perceptuomotora do robô. Entretanto, a língua é um produto cultural criado e adquirido através de interacções sociais. Isso destaca o papel da sociedade como fonte linguística. Tendo em conta estes aspectos, um cenário experimental foi definido no qual um instrutor humano ensina a um agente robótico os nomes dos objectos presentes num ambiente visualmente partilhado. O agente associa os nomes desses objectos à sua percepção visual desses objectos. A aprendizagem de palavras é um problema sem objectivo pré-estabelecido. Nós adquirimos novas palavras ao longo das nossas vidas. Assim, a arquitectura de aprendizagem do agente deve poder adquirir palavras e categorias de uma forma semelhante. Neste trabalho foram concebidas quatro arquitecturas de aprendizagem que podem ser usadas por agentes robóticos para aprendizagem e aquisição de novas palavras e categorias, incrementalmente. Os métodos de aprendizagem utilizados nestas arquitecturas foram projectados para funcionar de forma incremental, acumulando um conjunto cada vez maior de palavras e categorias. É proposta e aplicada uma nova metodologia da avaliação experimental que leva em conta a natureza aberta e incremental da aprendizagem de palavras. Esta metodologia leva em consideração a constatação de que o vocabulário de um robô será limitado pela sua capacidade de discriminação, a qual, por sua vez, depende dos seus sensores e capacidades perceptuais. Foi realizado um extenso conjunto de experiências sistemáticas em múltiplas situações experimentais, para avaliar cuidadosamente estas abordagens de aprendizagem. Os resultados indicam que todas as abordagens foram capazes de adquirir novas palavras e categorias incrementalmente. Embora em algumas das abordagens não tenha sido possível atingir vocabulários maiores, verificou-se que uma das abordagens conseguiu aprender até 293 categorias, com potencial para aprender muitas mais

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

High-dimensional and one-class classification

When dealing with high-dimensional data and, in particular, when the number of attributes p is large comparatively to the sample size n, several classification methods cannot be applied. Fisher's linear discriminant rule or the quadratic discriminant one are unfeasible, as the inverse of the involved covariance matrices cannot be computed. A recent approach to overcome this problem is based on Random Projections (RPs), which have emerged as a powerful method for dimensionality reduction. In 2017, Cannings and Samworth introduced the RP method in the ensemble context to extend to the high-dimensional domain classification methods originally designed for low-dimensional data. Although the RP ensemble classifier allows improving classification accuracy, it may still include redundant information. Moreover, differently from other ensemble classifiers (e.g. Random Forest), it does not provide any insight on the actual classification importance of the input features. To account for these aspects, in the first part of this thesis, we investigate two new directions of the RP ensemble classifier. Firstly, combining the original idea of using the Multiplicative Binomial distribution as the reference model to describe and predict the ensemble accuracy and an important result on such distribution, we introduce a stepwise strategy for post-pruning (called Ensemble Selection Algorithm). Secondly, we propose a criterion (called Variable Importance in Projection) that uses the feature coefficients in the best discriminant projections to measure the variable importance in classification. In the second part, we faced the new challenges posed by the high-dimensional data in a recently emerging classification context: one-class classification. This is a special classification task, where only one class is fully known (the target class), while the information on the others is completely missing. In particular, we address this task by using Gini's transvariation probability as a measure of typicality, aimed at identifying the best boundary around the target class

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one