4,897 research outputs found

    DeepOnto: A Python Package for Ontology Engineering with Deep Learning

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    Applying deep learning techniques, particularly language models (LMs), in ontology engineering has raised widespread attention. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present Deeponto, a Python package designed for ontology engineering. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to include other essential components including reasoning, verbalisation, normalisation, projection, and more. Building on this module, Deeponto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methodologies, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of Deeponto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).Comment: under review at Semantic Web Journa

    Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

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    Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease

    Modeling and Simulation in Engineering

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    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    Evolving Decision Rules with Geometric Semantic Genetic Programming

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceDue to the ever increasing amount of data available in today’s world, a variety of methods to harness this information are continuously being created, refined and utilized, drawing inspiration from a multitude of sources. Relevant to this work are Supervised Learning techniques, that attempt to discover the relationship between the characteristics of data and a certain feature, to uncover the function that maps input to output. Among these, Genetic Programming (GP) attempts to replicate the concept of evolution as defined by Charles Darwin, mimicking natural selection and genetic operators to generate and improve a population of solutions for a given prediction problem. Among the possible variants of GP, Geometric Semantic Genetic Programming (GSGP) stands out, due to its focus on the meaning of each individual it creates, rather than their structure. It achieves by imagining an hypothetical and perfect model, and evaluating the performance of others by measuring how much their behaviour differ from it, and uses a set of genetic operators that have a specific effect on the individual’s semantics (i.e., its predictions for training data), with the goal of reaching ever closer to the so called perfect specimen. This thesis conceptualizes and evaluates the performance of aGSGPimplementation made specifically to deal with multi-class classification problems, using tree-based individuals that are composed by a set of rules to allow the categorization of data. This is achieved through the careful translation of GSGP’s theoretical foundation, first into algorithms and then into an actual code library, able to tackle problems of this domain. The results demonstrate that the implementation works successfully and respects the properties of the the original technique, allowing us to obtain excellent results on training data, although performance on unseen data is a slightly worse than that of other state-of-the-art algorithms.Devido à crescente quantidade de dados do mundo de hoje, uma variedade de métodos para utilizar esta informação é continuamente criada, melhorada e utilizado, com inspiração de diversas fontes. Com particular relevância para este trabalho são técnicas de Supervised Learning, que visam descobrir a relação entre as características dos dados e um traço específico destes, de modo a encontrar uma função que consiga mapear os inputs aos outputs. Entre estas, Programação Genética (PG) tenta recriar o conceito de evolução como definido por Charles Darwin, imitando a seleção natural e operadores genéticos para gerar e melhorar uma população de soluções para um dado problema preditivo. Entre as possíveis variantes de PG, Programação Genética em Geometria Semântica (PGGS) é notável, pois coloca o seu foco no significado de cada indivíduo que cria, em vez da sua estrutura. Realiza isto ao imaginar um modelo hipotético e perfeito, e avaliar as capacidades dos outros medindo o quão diferente o seu comportamento difere deste, e utiliza um conjunto de operadores genéticos com um efeito específico na semântica de um indíviduo (i.e., as suas previsões para dados de treino), visando chegar cada vez mais perto ao tão chamado espécime perfeito. Esta tese conceptualiza e avalia o desempenho de uma implementação de PGGS feita especificamente para lidar com problemas de classificação multi-classe, utilizando indivíduos baseados em árvores compostos por uma série de regras que permitem a categorização de dados. Isto é feito através de uma tradução cuidadosa da base teórica de PGGS, primeiro para algoritmos e depois para uma biblioteca de código, capaz de enfrentar problemas deste domínio. Os resultados demonstram que a implementação funciona corretamente e respeita as propriedades da técnica original, permitindo que obtivéssemos resultados excelentes nos dados de treino, embora o desempenho em dados não vistos seja ligeiramente abaixo de outros algoritmos de última geração

    Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

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    High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions

    Self-supervised learning techniques for monitoring industrial spaces

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    Dissertação de mestrado em Matemática e ComputaçãoEste documento é uma Dissertação de Mestrado com o título ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces”e foi realizada e ambiente empresarial na empresa Neadvance - Machine Vision S.A. em conjunto com a Universidade do Minho. Esta dissertação surge de um grande projeto que consiste no desenvolvimento de uma plataforma de monitorização de operações específicas num espaço industrial, denominada SMARTICS (Plataforma tecnoló gica para monitorização inteligente de espaços industriais abertos). Este projeto continha uma componente de investigação para explorar um paradigma de aprendizagem diferente e os seus métodos - self-supervised learning, que foi o foco e principal contributo deste trabalho. O supervised learning atingiu um limite, pois exige anotações caras e dispendiosas. Em problemas reais, como em espaços industriais nem sempre é possível adquirir um grande número de imagens. O self-supervised learning ajuda nesses problemas, ex traindo informações dos próprios dados e alcançando bom desempenho em conjuntos de dados de grande escala. Este trabalho fornece uma revisão geral da literatura sobre a estrutura de self-supervised learning e alguns métodos. Também aplica um método para resolver uma tarefa de classificação para se assemelhar a um problema em um espaço industrial.This document is a Master’s Thesis with the title ”Self-Supervised Learning Techniques for Monitoring Industrial Spaces” and was carried out in a business environment at Neadvance - Machine Vision S.A. together with the University of Minho. This dissertation arises from a major project that consists of developing a platform to monitor specific operations in an industrial space, named SMARTICS (Plataforma tecnológica para monitorização inteligente de espaços industriais abertos). This project contained a research component to explore a different learning paradigm and its methods - self-supervised learning, which was the focus and main contribution of this work. Supervised learning has reached a bottleneck as they require expensive and time-consuming annotations. In real problems, such as in industrial spaces it is not always possible to require a large number of images. Self-supervised learning helps these issues by extracting information from the data itself and has achieved good performance in large-scale datasets. This work provides a comprehensive literature review of the self supervised learning framework and some methods. It also applies a method to solve a classification task to resemble a problem in an industrial space and evaluate its performance

    DataComp: In search of the next generation of multimodal datasets

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    Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms. To address this shortcoming in the ML ecosystem, we introduce DataComp, a testbed for dataset experiments centered around a new candidate pool of 12.8 billion image-text pairs from Common Crawl. Participants in our benchmark design new filtering techniques or curate new data sources and then evaluate their new dataset by running our standardized CLIP training code and testing the resulting model on 38 downstream test sets. Our benchmark consists of multiple compute scales spanning four orders of magnitude, which enables the study of scaling trends and makes the benchmark accessible to researchers with varying resources. Our baseline experiments show that the DataComp workflow leads to better training sets. In particular, our best baseline, DataComp-1B, enables training a CLIP ViT-L/14 from scratch to 79.2% zero-shot accuracy on ImageNet, outperforming OpenAI's CLIP ViT-L/14 by 3.7 percentage points while using the same training procedure and compute. We release DataComp and all accompanying code at www.datacomp.ai

    k-Means

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    Artificial Intelligence, Robots, and Philosophy

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    This book is a collection of all the papers published in the special issue “Artificial Intelligence, Robots, and Philosophy,” Journal of Philosophy of Life, Vol.13, No.1, 2023, pp.1-146. The authors discuss a variety of topics such as science fiction and space ethics, the philosophy of artificial intelligence, the ethics of autonomous agents, and virtuous robots. Through their discussions, readers are able to think deeply about the essence of modern technology and the future of humanity. All papers were invited and completed in spring 2020, though because of the Covid-19 pandemic and other problems, the publication was delayed until this year. I apologize to the authors and potential readers for the delay. I hope that readers will enjoy these arguments on digital technology and its relationship with philosophy. *** Contents*** Introduction : Descartes and Artificial Intelligence; Masahiro Morioka*** Isaac Asimov and the Current State of Space Science Fiction : In the Light of Space Ethics; Shin-ichiro Inaba*** Artificial Intelligence and Contemporary Philosophy : Heidegger, Jonas, and Slime Mold; Masahiro Morioka*** Implications of Automating Science : The Possibility of Artificial Creativity and the Future of Science; Makoto Kureha*** Why Autonomous Agents Should Not Be Built for War; István Zoltán Zárdai*** Wheat and Pepper : Interactions Between Technology and Humans; Minao Kukita*** Clockwork Courage : A Defense of Virtuous Robots; Shimpei Okamoto*** Reconstructing Agency from Choice; Yuko Murakami*** Gushing Prose : Will Machines Ever be Able to Translate as Badly as Humans?; Rossa Ó Muireartaigh**
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