14,789 research outputs found

    Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

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    We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for a given grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, S4Eq, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programsComment: 30 pages including appendi

    Countermeasures for the majority attack in blockchain distributed systems

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    La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació

    Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval

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    In computer-based search systems, similarity plays a key role in replicating the human search process. Indeed, the human search process underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. The search for images consists of establishing a correspondence between the available image and that sought by the user, by measuring the similarity between the images. Image search by content is generaly based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends notonly on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval (CBIR) system. In this article, first, we constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of characteristics extracted the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, noted type-2 fuzzy nearness measure (IT2FNM). By analogy to Type-2 Fuzzy Logic and motivated by near sets theory, we advanced a new fuzzy similarity measure (FSM) noted interval type-2 fuzzy nearness measure (IT-2 FNM). Then, we proposed three new IT-2 FSMs and we have provided mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e. reflexivity, transitivity, symmetry, and overlapping). Experimental results generated using three image databases showing consistent and significant results

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention

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    a reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected ... Bilateral lesions of the fastigial nucleus prevent the recovery of blood pressure following hypotension induced by ..

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

    Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos

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    Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. A well constructed data chart leads to an intuitive understanding of its underlying data and in the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Computer Vision techniques for image classification and object detection are widely used for the problem of recognizing charts, but only in images without any disturbance. Other features in real-world images that can make this task difficult are not present in most literature works, like photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. The task of reconstructing data is straightforward, as long the data is available the visualization can be reconstructed, but the scenario of reconstructing it on the same context is complex. Using a Visualization Grammar for this scenario is a key component, as these grammars usually have extensions for interaction, chart layers, and multiple views without requiring extra development effort. This work presents a model for automated support for custom recognition, and reconstruction of charts in images. The model automatically performs the process steps, such as reverse engineering, turning a static chart back into its data table for later reconstruction, while allowing the user to make modifications in case of uncertainties. This work also features a model-based architecture along with prototypes for various use cases. Validation is performed step by step, with methods inspired by the literature. This work features three use cases providing proof of concept and validation of the model. The first use case features usage of chart recognition methods focused on documents in the real-world, the second use case focus on vocalization of charts, using a visualization grammar to reconstruct a chart in audio format, and the third use case presents an Augmented Reality application that recognizes and reconstructs charts in the same context (a piece of paper) overlaying the new chart and interaction widgets. The results showed that with slight changes, chart recognition and reconstruction methods are now ready for real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando presentes nos meios de comunicação regulares, tais como jornais, revistas, páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma compreensão intuitiva dos seus dados inerentes e da mesma forma, quando os gráficos de dados têm escolhas de conceção erradas, poderá ser necessário um redesenho destas representações. Contudo, na maioria dos casos, estes gráficos são mostrados como uma imagem estática, o que significa que os dados originais não estão normalmente disponíveis. Portanto, poderiam ser aplicados métodos automáticos para extrair os dados inerentes das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande parte devido à variedade de tipos de gráficos e às suas características visuais. As técnicas de Visão Computacional para classificação de imagens e deteção de objetos são amplamente utilizadas para o problema de reconhecimento de gráficos, mas apenas em imagens sem qualquer ruído. Outras características das imagens do mundo real que podem dificultar esta tarefa não estão presentes na maioria das obras literárias, como distorções fotográficas, ruído, alinhamento, etc. Duas técnicas de visão computacional que podem ajudar nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e correção da perspetiva. Estes métodos transformam um gráfico distorcido e ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que os dados estejam disponíveis a visualização pode ser reconstruída, mas o cenário de reconstrução no mesmo contexto é complexo. A utilização de uma Gramática de Visualização para este cenário é um componente chave, uma vez que estas gramáticas têm normalmente extensões para interação, camadas de gráficos, e visões múltiplas sem exigir um esforço extra de desenvolvimento. Este trabalho apresenta um modelo de suporte automatizado para o reconhecimento personalizado, e reconstrução de gráficos em imagens estáticas. O modelo executa automaticamente as etapas do processo, tais como engenharia inversa, transformando um gráfico estático novamente na sua tabela de dados para posterior reconstrução, ao mesmo tempo que permite ao utilizador fazer modificações em caso de incertezas. Este trabalho também apresenta uma arquitetura baseada em modelos, juntamente com protótipos para vários casos de utilização. A validação é efetuada passo a passo, com métodos inspirados na literatura. Este trabalho apresenta três casos de uso, fornecendo prova de conceito e validação do modelo. O primeiro caso de uso apresenta a utilização de métodos de reconhecimento de gráficos focando em documentos no mundo real, o segundo caso de uso centra-se na vocalização de gráficos, utilizando uma gramática de visualização para reconstruir um gráfico em formato áudio, e o terceiro caso de uso apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos gráficos e widgets de interação. Os resultados mostraram que com pequenas alterações, os métodos de reconhecimento e reconstrução dos gráficos estão agora prontos para os gráficos do mundo real, tendo em consideração o tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic

    Códigos convolucionais para codificação em rede com múltiplos envios

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    In this thesis, we aim to provide a general overview of the area of multi-shot codes for network coding. We will review the approaches and results proposed so far and present slightly more general definitions of rank metric block and convolutional codes that allows a wider set of rates than the definitions of rank metric codes that exist in the literature. We also present, within this new framework, the notion of column rank distance of a rank metric convolutional code. We investigate it properties and derive an upper-bound that allows us to extend the notions of Maximum Distance Profile and Strongly-Maximum Distance Separable convolutional codes to some rank metric codes analogues. We focused on the development of channel encoders as a mechanism that allows the recovery of the data lost during the transmission. We also concentrate on the construction of novel classes of MRD convolutional codes. In particular we aim at extending the constructions presented by Napp, Pinto, Rosenthal and Vettori, in order to increase the degree of the code and consequently it error correction capability. As alternative to rank metric convolutional codes, we present a novel scheme by concatenation of a Hamming metric convolutional code (as outer code) and a rank metric block code (as a inner code). The proposed concatenated code is defined over the base finite field instead of over several extension finite fields and pretend to reduce the complexity of encoding and decoding process and moreover use the more general definition of rank metric code in order to be more natural.Nesta tese, pretendemos mostrar uma visão geral da área de códigos multishot na codificação em redes. Para o efeito, iremos rever as abordagens e resultados propostos até agora e apresentar definições um pouco mais gerais de códigos a blocos e códigos convolucionais que permitem uma ampliação das definições de códigos de métrica rank que já existem na literatura. Também apresentamos, dentro desta nova estrutura, a noção de distância de coluna de um código convolucional de métrica rank. Investigamos as suas propriedades e derivamos um limite superior para o valor da mesma, que nos permite estender as noções de MDP e Strongly MDS para os códigos de métrica rank. Iremos também focar-nos no desenvolvimento de codificadores de canal como mecanismo que permite uma melhor recuperação dos dados perdidos durante o processo de transmissão. Também nos concentramos na construção de novas classes de códigos convolucionais MRD. Em particular, pretendemos estender as construções apresentadas por Napp, Pinto, Rosenthal e Vettori, com o intuito de incrementar o grau do código e, consequentemente, melhorar a sua capacidade corretora. Como alternativa aos códigos convolucionais de métrica rank, apresentamos um novo esquema usando concatenação de um código convolucional de métrica Hamming (como código externo) e um código a bloco de métrica rank (como um código interno). O código concatenado proposto é definido sobre o corpo finito base, com o intuito de reduzir a complexidade do processo de codificação e decodificação e, além disso, usa a definição mais geral de código de métrica rank, tornando o processo mais natural.Programa Doutoral em Matemátic
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