2,825 research outputs found

    Integrating expert-based objectivist and nonexpert-based subjectivist paradigms in landscape assessment

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    This thesis explores the integration of objective and subjective measures of landscape aesthetics, particularly focusing on crowdsourced geo-information. It addresses the increasing importance of considering public perceptions in national landscape governance, in line with the European Landscape Convention's emphasis on public involvement. Despite this, national landscape assessments often remain expert-centric and top-down, facing challenges in resource constraints and limited public engagement. The thesis leverages Web 2.0 technologies and crowdsourced geographic information, examining correlations between expert-based metrics of landscape quality and public perceptions. The Scenic-Or-Not initiative for Great Britain, GIS-based Wildness spatial layers, and LANDMAP dataset for Wales serve as key datasets for analysis. The research investigates the relationships between objective measures of landscape wildness quality and subjective measures of aesthetics. Multiscale geographically weighted regression (MGWR) reveals significant correlations, with different wildness components exhibiting varying degrees of association. The study suggests the feasibility of incorporating wildness and scenicness measures into formal landscape aesthetic assessments. Comparing expert and public perceptions, the research identifies preferences for water-related landforms and variations in upland and lowland typologies. The study emphasizes the agreement between experts and non-experts on extreme scenic perceptions but notes discrepancies in mid-spectrum landscapes. To overcome limitations in systematic landscape evaluations, an integrative approach is proposed. Utilizing XGBoost models, the research predicts spatial patterns of landscape aesthetics across Great Britain, based on the Scenic-Or-Not initiatives, Wildness spatial layers, and LANDMAP data. The models achieve comparable accuracy to traditional statistical models, offering insights for Landscape Character Assessment practices and policy decisions. While acknowledging data limitations and biases in crowdsourcing, the thesis discusses the necessity of an aggregation strategy to manage computational challenges. Methodological considerations include addressing the modifiable areal unit problem (MAUP) associated with aggregating point-based observations. The thesis comprises three studies published or submitted for publication, each contributing to the understanding of the relationship between objective and subjective measures of landscape aesthetics. The concluding chapter discusses the limitations of data and methods, providing a comprehensive overview of the research

    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback

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    We propose a method to capture the handling abilities of fast jet pilots in a software model via reinforcement learning (RL) from human preference feedback. We use pairwise preferences over simulated flight trajectories to learn an interpretable rule-based model called a reward tree, which enables the automated scoring of trajectories alongside an explanatory rationale. We train an RL agent to execute high-quality handling behaviour by using the reward tree as the objective, and thereby generate data for iterative preference collection and further refinement of both tree and agent. Experiments with synthetic preferences show reward trees to be competitive with uninterpretable neural network reward models on quantitative and qualitative evaluations

    Agricultural investment behaviour and contingency: Experimental evidence from Uganda

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    Underinvestment in agriculture – a major cause of rural poverty – may be due to difficulties in detecting ‘contingency’, defined as the influence one may exert on the outcome of a decision-making situation. Recently experienced contingency may create a mismatch between perceived and actual contingency in an investment decision-making situation, leading to sub-optimal investment behaviour. To test this, we use an experiment with poor farmers in Uganda used to low levels of contingency, as many factors (e.g., the weather, pests, price fluctuations) obscure the link between farm investment and outcomes. We find that in situations in which some contingency is present, investment levels respond positively to recently experienced contingency. In situations in which no contingency is present (‘non-contingency’), investment responds negatively to recently experienced non-contingency. The findings that perceived contingency influences investment behaviour, and perceived contingency can be readily changed, may inform new behavioural policies to promote agricultural investment

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Digital support for alcohol moderation and smoking cessation in cancer survivors

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    Machine Learning-powered Course Allocation

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    We introduce a machine learning-powered course allocation mechanism. Concretely, we extend the state-of-the-art Course Match mechanism with a machine learning-based preference elicitation module. In an iterative, asynchronous manner, this module generates pairwise comparison queries that are tailored to each individual student. Regarding incentives, our machine learning-powered course match (MLCM) mechanism retains the attractive strategyproofness in the large property of Course Match. Regarding welfare, we perform computational experiments using a simulator that was fitted to real-world data. Our results show that, compared to Course Match, MLCM increases average student utility by 4%-9% and minimum student utility by 10%-21%, even with only ten comparison queries. Finally, we highlight the practicability of MLCM and the ease of piloting it for universities currently using Course Match

    Personalized ECA Tutoring with Self-Adjusted POMDP Policies and User Clustering

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    An Embodied Conversational Agent (ECA) is an intelligent agent that enables real-time human/computer interaction in natural language. For its rich style of communication, ECA is particularly popular and useful in applications such as education, e-commerce, healthcare, finance, marketing, and business, where a human-like conversation is more attractive to users than traditional keyboard-based interaction. The interest in using ECA in e-learning has become even stronger since the COVID-19 outbreak, and a preliminary investigation has been started by our research group to extend collaborative learning in a virtual environment with personalized ECA tutoring. This thesis document first highlights the prior work of personalized tutoring with ECA, including wavelet transformation for user clustering and face-to-face interaction for quiz-style e-learning. An enhanced approach is then developed to enable self-adjustment of POMDP policies for dialogue management and to allow a more natural way of question/answer style of personalized tutoring with a generic, flexible tutoring ontology. In addition, the proposed approach uses machine learning techniques to adjust knowledge levels of user clustering and evaluates its effectiveness by conducting experiments with real datasets. This research work is projected to further improve online learning with ECA serving as a personal tutor

    Body perception and brain plasticity in blind and sighted individuals

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    Lack of vision is associated with large-scale brain plasticity. Vision, touch, proprioception, interoception, and other sensory modalities are thought to play a vital role in developing and maintaining bodily awareness. How do blind people perceive their bodies, and what kind of compensatory neuroplasticity processes are involved? This thesis comprises a series of experiments focused on a profoundly understudied topic – the perception of one’s body following blindness. Study I shows that blind individuals are significantly better at perceiving their heartbeats than sighted individuals. The results indicate that blind individuals experience signals from inner organs differently than sighted individuals, which has implications for further research on emotional processing and bodily awareness. Study II provides a broader insight into tactile perception following blindness by studying discriminative and affective touch plasticity in blind and sighted groups. A key novel finding is changed pleasantness sensation due to affective touch, that is, slow, gentle, caress-like stroking of the skin, especially on the palm, in blind participants compared to sighted participants. The results have implications for understanding social and physical interactions in blind individuals. Study III re-examines a classic paradigm to study multisensory bodily awareness, the somatic rubber hand illusion, in a large sample of blind participants with a well-matched sighted control group. The results present strong evidence that blind individuals are “immune” to this illusion which suggests that they rely more on unisensory processing rather than multimodal integration of sensory signals, compared to sighted individuals. Study IV investigates the effect of short-term visual deprivation by a two-hour blindfolding procedure on the bodily senses of cardiac interoception, thermosensation, and discriminative touch in sighted participants. The results show no effect on these senses, which suggests that the changes observed in blind individuals on these sensory functions relate to their long-term lack of visual experience and associated brain plasticity changes. Finally, Study V uses structural magnetic resonance imaging to analyze cortical thickness in a group of blind individuals and a matched sighted control group and relate the cortical thickness measure to the behaviorally registered changes in cardiac interoceptive accuracy. The key finding is that blind individuals with thicker occipital cortices are better at sensing their heartbeats; this finding advances our understanding of the limits of cross-modal plasticity following blindness and suggests that the visual cortex supports the awareness of inner bodily sensations in blind individuals. Overall, this thesis is the first systematic characterization of differences and similarities between blind and sighted individuals in body perception and functioning of the bodily senses, opening a line of research with important links to mental health

    Swallow, breathing and survival: sex-specific effects of opioids.

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    This dissertation presents a series of studies examining mechanisms of deglutition and respiration, and how these vital processes are impacted by opioids. The experiments in Chapter Two investigated the role of the upper esophagus in airway protection through systematic activation of pharyngeal and esophageal mechanoreceptors in a cat electromyography model. Chapter Three compared effects of opioid administration on breathing and swallowing between male and female rats, and found that females are more susceptible to opioid-induced depression of breathing and swallow than males. Findings from Chapters Two and Three led to the development of a translational model of opioid-induced dysphagia using videofluoroscopy. Chapter Four demonstrated that opioid administration resulted in a significant decline in airway protection during swallow in freely feeding, unrestrained cats. This work has advanced knowledge of the regulation of the upper aerodigestive tract, and its dual roles in breathing and swallowing. An improved understanding of the neural control of deglutition will facilitate the development of effective treatments for dysphagia. This dissertation includes the first study to compare effects of opioids on pharyngeal swallow between sexes, and provides mechanistic and clinically-translatable insights into opioid-induced dysphagia. Elucidating the actions of opioids on the brainstem breathing and swallowing networks will aid the prevention and treatment of opioid-induced respiratory depression and dysphagia related complications such as aspiration pneumonia
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