6,763 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Explainable fault prediction using learning fuzzy cognitive maps

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    IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short-Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state-of-the-art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what-if analysis, could provide more information about the black-box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability

    Utilitarianism and the Social Nature of Persons

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    This thesis defends utilitarianism: the view that as far as morality goes, one ought to choose the option which will result in the most overall well-being. Utilitarianism is widely rejected by philosophers today, largely because of a number of influential objections. In this thesis I deal with three of them. Each is found in Bernard Williams’s ‘A Critique of Utilitarianism’ (1973). The first is the Integrity Objection, an intervention that has been influential whilst being subject to a wide variety of interpretations. In Chapter Two I give my interpretation of Williams’s Integrity objection; in Chapter Three I discuss one common response to it, and in Chapters Four and Five I give my own defence of utilitarianism against it. In Chapter Six I discuss a second objection: the problem of pre-emption. This problem is also found in Williams, but has received greater attention through the work of other authors in recent years. It suggests that utilitarianism is unable to deal with some of the modern world’s most pressing moral problems, and raises an internal tension between the twin utilitarian aims of making a difference and achieving the best outcomes. In Chapter Seven I discuss a third objection: that utilitarianism is insufficiently egalitarian. I find this claim to be unwarranted, in light of recent social science and philosophy. My responses to Williams’s objections draw upon resources from the socialist tradition – in particular, that tradition’s emphasis on the importance of social connections between individuals. Socialists have often been hostile to utilitarianism, in part for socialist-inflected versions of Williams’s objections. Thus, in responding to these objections I aim to demonstrate that socialist thought contains the means to defuse not only mainstream philosophy’s rejection of utilitarianism but also its own, and thus to re-open the possibilities for a productive engagement between the two traditions

    A field-based computing approach to sensing-driven clustering in robot swarms

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    Swarm intelligence leverages collective behaviours emerging from interaction and activity of several “simple” agents to solve problems in various environments. One problem of interest in large swarms featuring a variety of sub-goals is swarm clustering, where the individuals of a swarm are assigned or choose to belong to zero or more groups, also called clusters. In this work, we address the sensing-based swarm clustering problem, where clusters are defined based on both the values sensed from the environment and the spatial distribution of the values and the agents. Moreover, we address it in a setting characterised by decentralisation of computation and interaction, and dynamicity of values and mobility of agents. For the solution, we propose to use the field-based computing paradigm, where computation and interaction are expressed in terms of a functional manipulation of fields, distributed and evolving data structures mapping each individual of the system to values over time. We devise a solution to sensing-based swarm clustering leveraging multiple concurrent field computations with limited domain and evaluate the approach experimentally by means of simulations, showing that the programmed swarms form clusters that well reflect the underlying environmental phenomena dynamics

    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

    Investigating the aftermath of the TĂŒrkiye 2023 earthquake: exploring post-disaster uncertainty among Syrian migrants using social network analysis with public health approach

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    ObjectivesOn February 6th, 2023, a doublet earthquake struck TĂŒrkiye, impacting more than 15 million people including migrants, and resulting in over 50,000 deaths. The Syrian migrants experience multiple uncertainties in their daily lives which are further compounded by multifaceted challenges of the post-disaster environment. Social media was used intensively and with impunity in this environment and thereby provides a window into the explicit and implicit dynamics of daily life after a disaster. We aimed to explore how a post-disaster environment potentially generates new uncertainties or exacerbating pre-existing ones for migrants through social media analysis with an indirect perspective, in the context of 2023-Earthquake in TĂŒrkiye and Syrian migrants.MethodsSocial network analysis was used to analyze Twitter-data with the hashtags ‘Syrian’ and ‘earthquake’ during a 10-day period beginning on March 22nd, 2023. We calculated network metrics, including degree-values and betweenness-centrality and clustered the network to understand groups. We analyzed a combination of 27 tweets with summative content analysis using a text analysis tool, to identify the most frequently used words. We identified the main points of each tweet and assessed these as possible contributors to post-disaster uncertainty among migrants by using inductive reasoning.ResultsThere were 1918 Twitter users, 274 tweets, 124 replies and 1726 mentions. Discussions about Syrian migrants and earthquakes were established across various groups (ngroups(edges > 15) = 16). Certain users had a greater influence on the overall network. The nine most frequently used words were included under uncertainty-related category (nmost_frequently_used_words = 20); ‘aid, vote, house, citizen, Afghan, illegal, children, border, and leave’. Nine main points were identified as possible post-disaster uncertainties among migrants.ConclusionThe post-disaster environment has the potential to exacerbate existing uncertainties, such as being an undocumented migrant, concerns about deportation and housing, being or having a child, inequality of rights between being a citizen and non-citizen, being in minority within minority, political climate of the host nation and access to education or to generate new ones such equitable distribution of aid, which can lead to poor health outcomes. Recognizing the possible post-disaster uncertainties among migrants and addressing probable underlying factors might help to build more resilient and healthy communities

    Trustworthy Federated Learning: A Survey

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    Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

    Peer-to-Peer Trading for Enhancing Electric Vehicle Charging with Renewable Energy

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    Electric vehicles (EVs) are rapidly increasing in popularity as greater attention is paid to climate change and decarbonisation, however the environmental benefits that EVs offer can only be fully realised through the use of renewable energy for their charging. Smart charging solutions are essential for managing the impact of EVs and increasing the utilisation of renewable energy, however, questions remain over whether low-voltage distribution networks can accommodate the upcoming increases in EV charging demand. This thesis addresses both the challenge of increasing the utilisation of renewable energy for EV charging and also the importance of ensuring safe operation of low-voltage distribution networks with the integration of EV charging, distributed renewable energy generation, battery storage and vehicle-to-grid technologies. Chapter 3 examines a scenario where houses equipped with solar photovoltaic panels and EV charge points endeavour to sell surplus solar energy and the use of their EV charge point to visiting EVs that require charging. A peer-to-peer auction is proposed, with a novel matching mechanism presented to increase the amount of EV charging completed using solar energy without any knowledge about future EV arrivals. Chapter 4 presents a full peer-to-peer trading model of Network Impact Tokens and Phase Impact Tokens between houses in a low-voltage network. The Impact Tokens guarantee that all EV charging and renewable energy generation does not cause the network to exceed its voltage, current or transformer loading limits, while ensuring each house retains control over its energy usage, requiring no real-time monitoring or sensors in the network, and no privacy issues are encountered. The Network and Phase Impact Token approach is further verified in Chapter 5, as it forms the basis of a novel approach for Distribution System Operators to evaluate the maximum EV hosting capacity of their networks in conjunction with renewable energy generation and battery storage. The maximum EV capacity results are verified by an alternate Optimisation approach and the maximum EV penetration is evaluated for a number of scenarios

    Multisensory processing, affect and multimodal manipulation: A cognitive-semiotic empirical study of travel documentaries

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    Multisensory processing represents the mirror image of multimodal meaning-making, in that interpreting multimodal discourse predominantly requires multisensory processing, even when different modes rely on the same sensory channels (Khateb et al., 2002), for example images and text in a book (Gibbons, 2012, p. 40). Remley (2017) makes a similar point when discussing the neuroscience of multimodal persuasive messages, when he asserts that “[t]he term ‘multisensory integration’ is the biological equivalent of the term ‘multimodal’ in rhetoric” (p. 9). An understanding of multisensory processing can therefore be (and presumably is) exploited at the stage of text-production as a resource for manipulative multimodal discourses, with all the ideological consequences that entails. The concept of manipulation has been a matter of discussion in critical discourse studies (CDS) and pragmatics for more than a decade. Agreement on how to define and analyse the latter has yet to be reached, although most scholars seem to agree that Relevance Theory (Sperber and Wilson, 1995) can provide a useful entry point thanks to its theorisation of variable contexts and individual cognitive environments (de Saussure, 2005; Maillat, 2013; Maillat and Oswald, 2009; Oswald, 2014). Moreover, the concept of epistemic vigilance (Sperber et al., 2010) has been used to investigate the cognitive barriers that need to be bypassed in order for manipulation to work (Hart, 2013; Mazzarella, 2015). Finally, Sorlin (2017: 133) recently highlighted the need to focus not only on the cognitive aspects influencing manipulation, but also on “the psychological aspect of manipulation that often consists in exploiting the target's weaknesses”, thus pointing towards the dimension of affect as a further explanatory force. This paper begins with an overview of the concepts of manipulation and epistemic vigilance, before discussing insights from the field of multisensory processing in the neurosciences. Then, drawing on some principles from Relevance Theory (Sperber and Wilson, 1995) and looking at some data from travel documentary programmes and their viewers, examples are offered of how manipulation is attempted and achieved through this specific multimodal genre in individual case studies. The focus of the analysis will be on bottom-up (i.e. text-driven) processes and the interpretation/reaction of an audience. The research draws on a novel methodological approach (Castaldi, 2021) that integrates Audience Research (e.g., Schrþder et al., 2003) and Social Semiotics (e.g. Kress and van Leeuwen, 1996, 2001; van Leeuwen, 1999; Machin and Mayr, 2012) in order to analyse media interactions in their individuality. Results suggest that the affective dimension, predominantly attended to through sonic and visual modes, plays a key role for multimodal manipulation to successfully occur
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