944 research outputs found

    Making the third mission possible: investigating academic staff experiences of community-engaged learning

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    Community-engaged Learning (CEL) is an intentional and structured pedagogical approach, which links learning objectives with community needs. Most of the existing literature is centred on Service-learning practice in the United States. To date, there have been no in-depth studies on the experiences and perspectives of practitioners who engage with CEL in a UK or more specifically, a Scottish Higher Education context. The thesis presents data collected from a qualitative study utilising documentary analysis of government and institutional literature and 23 in-depth interviews with University practitioners, managers and leaders. I explored factors which influence the perspectives and experiences of CEL practitioners at one Scottish, research-intensive Russell Group university. Adopting a research ontology informed by Margaret Archer’s Morphogenetic, Critical Realist approach, I analyse the data collected through the lens of an emancipatory Neo-Aristotelian virtue-ethics framework and argue that CEL practice at this university contributes to, what the evidence suggests is, its ultimate purpose: promoting and cultivating individual flourishing and emancipatory critical thinking for the common good. Focussing on university-community engagement, the findings suggest that there are some inconsistencies between how the University is portrayed in public-facing literature compared to the level of institutional support individual practitioners of CEL report receiving. I conclude that failure to adequately support CEL activity in the future could negatively impact the sustainability and quality of community engagement at Alba University

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

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    Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable

    The Role of Synthetic Data in Improving Supervised Learning Methods: The Case of Land Use/Land Cover Classification

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    A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information ManagementIn remote sensing, Land Use/Land Cover (LULC) maps constitute important assets for various applications, promoting environmental sustainability and good resource management. Although, their production continues to be a challenging task. There are various factors that contribute towards the difficulty of generating accurate, timely updated LULC maps, both via automatic or photo-interpreted LULC mapping. Data preprocessing, being a crucial step for any Machine Learning task, is particularly important in the remote sensing domain due to the overwhelming amount of raw, unlabeled data continuously gathered from multiple remote sensing missions. However a significant part of the state-of-the-art focuses on scenarios with full access to labeled training data with relatively balanced class distributions. This thesis focuses on the challenges found in automatic LULC classification tasks, specifically in data preprocessing tasks. We focus on the development of novel Active Learning (AL) and imbalanced learning techniques, to improve ML performance in situations with limited training data and/or the existence of rare classes. We also show that much of the contributions presented are not only successful in remote sensing problems, but also in various other multidisciplinary classification problems. The work presented in this thesis used open access datasets to test the contributions made in imbalanced learning and AL. All the data pulling, preprocessing and experiments are made available at https://github.com/joaopfonseca/publications. The algorithmic implementations are made available in the Python package ml-research at https://github.com/joaopfonseca/ml-research

    Performing Both Sides of the Glass: Videogame Affordances and Live Streaming on Twitch

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    This thesis examines the performative dimensions videogame affordances assume within online, live streaming environments. This approach considers how streamers configure their videogame play in terms of a potential audience, drawing on five semi-structured, in-depth interviews with Australian-based Twitch streamers to analyse how streamers leverage videogame affordances to produce “meaningful moments”. Guiding this thesis is the question of how the player-videogame relationship is maintained, fractured or altered within live-streaming environments such as Twitch

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Fictional Practices of Spirituality I: Interactive Media

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    "Fictional Practices of Spirituality" provides critical insight into the implementation of belief, mysticism, religion, and spirituality into worlds of fiction, be it interactive or non-interactive. This first volume focuses on interactive, virtual worlds - may that be the digital realms of video games and VR applications or the imaginary spaces of life action role-playing and soul-searching practices. It features analyses of spirituality as gameplay facilitator, sacred spaces and architecture in video game geography, religion in video games and spiritual acts and their dramaturgic function in video games, tabletop, or LARP, among other topics. The contributors offer a first-time ever comprehensive overview of play-rites as spiritual incentives and playful spirituality in various medial incarnations
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