1,092 research outputs found

    Supervised classifiers to identify hate speech on English and Spanish tweets

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    Consistently with social and political concern about hatred and harassment through social media, in recent years, automatic hate-speech detection and offensive behavior in social media are gaining a lot of attention. In this paper, we examine the performance of several supervised classifiers in the process of identifying hate speech on Twitter. More precisely, we do an empirical study that analyzes the influence of two types of linguistic features (n-grams, word embeddings) when they are used to feed different supervised machine learning classifiers: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Complement Naive Bayes (CNB), Decision Tree (DT), Nearest Neighbors (KN), Random Forest (RF) and Neural Network (NN). The experiments we have carried out show that CNB, SVM, and RF are better than the rest classifiers in English and Spanish languages by taking into account all features.Xunta de Galicia | Ref. ED431C 2018/50Agencia Estatal de Investigación | Ref. TIN2017-85160-C2-2-

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Modular lifelong machine learning

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    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

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    The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it. To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    A Sentiment Analysis of Filipinx on Twitter Using a Multinomial Naïve Bayes Classification Model

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    On social media, the use of “Filipinx” as a gender neutral, inclusive term for “Filipino” tends to generate high user engagement, at times without regard for the original context in which the word appears. This project applies computational methods to collect a large dataset in English/Filipino from Twitter containing “Filipinx”, and to train a Naïve Bayes model to classify tweets into three sentiments: positive, neutral, and negative. My methodology takes inspiration from that of four related studies that similarly conducted sentiment analysis on English/Filipino tweets involving various topics, and whose resulting accuracy scores were compared side-by-side. Conducting sentiment analysis on tweets that mention “Filipinx” would meet four goals: to compare the model’s performance with those from the previous four studies, to create a larger-scale picture of user sentiments about the use of “Filipinx” than what I previously presented in a small-scale sociolinguistics project, and to contribute to conversations on how Filipino social media users discursively define Filipino identity
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