587 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    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

    Video Summarization Using Unsupervised Deep Learning

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    In this thesis, we address the task of video summarization using unsupervised deep-learning architectures. Video summarization aims to generate a short summary by selecting the most informative and important frames (key-frames) or fragments (key-fragments) of the full-length video, and presenting them in temporally-ordered fashion. Our objective is to overcome observed weaknesses of existing video summarization approaches that utilize RNNs for modeling the temporal dependence of frames, related to: i) the small influence of the estimated frame-level importance scores in the created video summary, ii) the insufficiency of RNNs to model long-range frames' dependence, and iii) the small amount of parallelizable operations during the training of RNNs. To address the first weakness, we propose a new unsupervised network architecture, called AC-SUM-GAN, which formulates the selection of important video fragments as a sequence generation task and learns this task by embedding an Actor-Critic model in a Generative Adversarial Network. The feedback of a trainable Discriminator is used as a reward by the Actor-Critic model in order to explore a space of actions and learn a value function (Critic) and a policy (Actor) for video fragment selection. To tackle the remaining weaknesses, we investigate the use of attention mechanisms for video summarization and propose a new supervised network architecture, called PGL-SUM, that combines global and local multi-head attention mechanisms which take into account the temporal position of the video frames, in order to discover different modelings of the frames' dependencies at different levels of granularity. Based on the acquired experience, we then propose a new unsupervised network architecture, called CA-SUM, which estimates the frames' importance using a novel concentrated attention mechanism that focuses on non-overlapping blocks in the main diagonal of the attention matrix and takes into account the attentive uniqueness and diversity of the associated frames of the video. All the proposed architectures have been extensively evaluated on the most commonly-used benchmark datasets, demonstrating their competitiveness against other approaches and documenting the contribution of our proposals on advancing the current state-of-the-art on video summarization. Finally, we make a first attempt on producing explanations for the video summarization results. Inspired by relevant works in the Natural Language Processing domain, we propose an attention-based method for explainable video summarization and we evaluate the performance of various explanation signals using our CA-SUM architecture and two benchmark datasets for video summarization. The experimental results indicate the advanced performance of explanation signals formed using the inherent attention weights, and demonstrate the ability of the proposed method to explain the video summarization results using clues about the focus of the attention mechanism

    Undergraduate Catalog of Studies, 2022-2023

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    How ChatGPT is changing digital marketing activities: The Aleluya’s Case.

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    openIn recent years, several terms related to artificial intelligence have come to the forefront of everyday conversations, leaving us both delighted and confused about its scope. Initially, this paper shows that AI is not something that was born sporadically in recent years, on the contrary it is something that has been worked on for more than half a decade and has had major milestones in its history. One of those milestones is ChatGPT, an AI-powered chatbot capable of generating human-like text responses in a conversational manner. This new technology holds great promise in almost every industry, and this paper focuses on evidencing the different scenarios in which ChatGPT can transform companies' marketing activities, making them more efficient, innovative and user-friendly. But it also considers its implications and challenges. Finally, a case study is shown in the Colombian startup Aleluya, in which an experiment was carried out implementing ChatGPT in their marketing activities in order to generate an appropriation of this new technology and to see in practice its advantages and disadvantages

    An “other” experience of videogames: analyzing the connections between videogames and the lived experience of chronic pain

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    In this dissertation I argue for the connections between the lived experience of chronic pain and videogames, exploring what interacts with and influences them. To answer this, I draw on cripistemology as I engage in autoethnography, close-reading and close-gameplay, restorying, mixed methods design, formal interviews, surveys, and inductive coding. I further argue for pushing back against the unhelpful binaries that define the “human” and a false idea of “universal” experience or ability, instead pointing to the intersectionality that better reflects the biopolitics of disability, including both debility and capacity. I engage with these methods in three specific projects that consider additional sub-questions to further tease out why videogames disability, chronic pain, game design, lived experience, human centered design, embodiment in video games have impacted me so deeply and how this ties to my identity as a disabled woman. I further offer this dissertation to highlight the growing research of lived experience and disability in the field of game studies, providing empirical data that offers a foundational look of how I as a member of the chronic pain community think and feel about videogames, as well as how a small portion of the chronic pain community discusses videogames and the range of experiences this encompasses. In doing so, I unpack and argue on the relationship that exists between chronic pain and videogames, and further articulate why this matters. In Chapter 1 I provide necessary history and information regarding my research to better articulate the findings as presented in the following chapters. In Chapter 2, I analyze my connection to Animal Crossing: New Leaf (AC:NL) (Nintendo EAD, 2012) and explore opportunities about genre and mechanics as reflections of my own daily lived experience with chronic pain, especially including my experience in a 2014 pain rehabilitation program. Through this process, I define the “slice of life” genre and argue that AC:NL is exemplary of its markers. In Chapter 3 I provide a deep reading and analysis of Nintendo’s GameCube release Chibi-Robo! (Skip Ltd. et al., 2005) to “restory” the titular main character to have chronic pain like my own. Through the lens of debility and capacitation machines, I map these ideas onto the biopsychosocial model to organize a thorough analysis of his restoried identity. In modding the game’s narrative to reflect a lived experience of chronic pain like my own, I interweave fanfiction with deep reading and deep gameplay to unpack what representation I am looking for in videogames both narratively and mechanically. In this I further argue how this practice can be used to inform future game design. Finally, in Chapter 4, I interview members of the chronic pain community to understand their perspective on the connections between their lived experience with chronic pain and videogames, as well as how additional factors of their identity impact those experiences. For this I engage in a mixed methods design to conduct a survey and formal interviews to offer foundational work on how the chronic pain community interacts with videogames. I offer this project to intersect current research in chronic pain and videogames (and its related technology) that focuses on games as tools for “curing” pain, and argue the importance of considering what embodiment people with chronic pain already have in videogames instead. Ultimately, I argue for the necessity to complicate current design practices in human centered design (HCD) and game design. To do so, I highlight the lived experience of Othered identities to combat misguided notions of “universal” intent. In this, I analyze the inherent connections between videogames and disability, in this case chronic pain, through embodiment and lived experience. I center in on how my experience of chronic pain has impacted the way in which I engage and think about with videogames, and further, how my experiences align with that of the chronic pain community

    Essays on Innovations in Public Sector Auditing

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    The current antecedents of innovation in the public sector, that is, the adoption of SDGs and the unprecedented technological advancements exert pressures on the Supreme audit institutions’(SAIs) current socio-technical system. This has led SAIs to adopt different strategies to maintain their relevance and improve the quality of their work and operations. This thesis investigated the different types of innovations currently happening in the SAIs environment and how SAIs are reacting to the demands of these changes. This exploratory work captured public sector audit innovation through the following three essays: The first essay focused on Digital Transformation (DT), investigated how SAIs approach, and interpret DT. In this regard, DT was investigated from a SAIs perspective. Due to it being a novel topic in public sector auditing research, a qualitative research method was adopted, this method was supported with expert interviews and archival and or document data. Key findings revealed that the definition of DT varies from SAI to SAI, and this variation resulted from the differences in the level of digital development in each country. SAIs applied reactive and, in some situations proactive change strategies were applied. In the reactive strategy, SAIs reacted to change induced by a situational demand while in the proactive strategy, they experiment with technologies in advance. Most of the SAIs applying proactive change strategy operates an innovation lab or an experimentation space(see Bojovic, Sabatier, and Coblence 2020; Bucher and Langley 2016; Cartel, Boxenbaum, and Aggeri 2019; Wulf 2000). As an impact on public sector auditing profession, the research addresses the popular narrative of SAI’s equating digitization or the use of digital technologies to Digital transformation. It reiterated the holistic nature of DT, by pointing at the risk involved when DT is tied solely to technology adoption strategy ignoring other aspects such as people, organizational structure, strategy, culture, etc.La trasformazione in corso dell'ambiente esterno delle Istituzioni Superiori di Controllo (ISC, Corte dei conti) sta modificando le esigenze di controllo e le aspettative dei vari stakeholders coinvolti. Infatti, questa trasformazione, innescato dai progressi tecnologici, dall'adozione degli Obiettivi di Sviluppo Sostenibile (OSS) e dalla trasparenza sta modificando il modo e gli strumenti con cui viene esercitata l’attivitĂ  di controllo. CiĂČ ha portato le ISC a adottare diverse strategie ed a introdurre diverse innovazioni per mantenere la loro rilevanza e migliorare la qualitĂ  del loro servizio. Vari autori hanno evidenziato la necessitĂ  di indagare circa le implicazioni del cambio della strategia di controllo e dell’adozione delle varie innovazioni tecnologiche nelle ISC. Il lavoro di tesi contribuisce in questa direzione e indaga sulle varie innovazioni tecnologiche adottate dalle ISC e come questi Istituzioni hanno reagito alle pressioni esterne di cambiamento. La tesi adotta un approccio esplorativo e sviluppa tre diverse ricerche per rispondere alla domanda principale di ricerca. La prima ricerca si concentra sulla trasformazione digitale (TD), e indaga su come le ISC hanno affrontato e interpretato la TD. La metodologia utilizzata Ăš di tipo qualitativo. Sono state effettuate varie interviste a esperti del settore a livello internazionale oltre all’analisi documentale degli archivi delle varie istituzioni analizzate. I risultati hanno mostrato una diversa interpretazione e percezione, tra le istituzioni oggetto dello studio, del concetto della TD, dovuta alle differenze di sviluppo digitale nei vari paesi analizzati. Inoltre, i risultati mostrano che le ISC hanno adottato strategie reattive di cambiamento e, in alcune situazioni, hanno adottato strategie proattive. Nel primo caso, che rappresenta la maggioranza dei casi analizzati, le ISC hanno reagito al bisogno ovvero quando si presenta una necessitĂ  di cambiamento. Mentre nel secondo caso, ovvero di strategia di cambiamento proattivo, le ISC hanno sperimentato le tecnologie in anticipo. La maggior parte delle Istituzioni che ha adottato strategie proattive di cambiamento gestisce un laboratorio di innovazione o uno spazio di sperimentazione (vedi Bojovic, Sabatier e Coblence 2020; Bucher e Langley 2016; Cartel, Boxenbaum e Aggeri 2019; Wulf 2000). Inoltre, la ricerca mostra come la digitalizzazione o l'uso delle tecnologie digitali vengono equiparati alla TD nelle ISC. Questo rischio di interpretazione del concetto si concretizza soprattutto, come mostrano i risultati, quando la TD viene legata esclusivamente alla strategia di adozione della tecnologia ignorando altri aspetti come le persone, la struttura organizzativa, la strategia, la cultura, ecc

    Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

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    Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

    Spatiotemporal Event Graphs for Dynamic Scene Understanding

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    Dynamic scene understanding is the ability of a computer system to interpret and make sense of the visual information present in a video of a real-world scene. In this thesis, we present a series of frameworks for dynamic scene understanding starting from road event detection from an autonomous driving perspective to complex video activity detection, followed by continual learning approaches for the life-long learning of the models. Firstly, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle’s ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. Due to the lack of datasets equipped with formally specified logical requirements, we also introduce the ROad event Awareness Dataset with logical Requirements (ROAD-R), the first publicly available dataset for autonomous driving with requirements expressed as logical constraints, as a tool for driving neurosymbolic research in the area. Next, we extend event detection to holistic scene understanding by proposing two complex activity detection methods. In the first method, we present a deformable, spatiotemporal scene graph approach, consisting of three main building blocks: action tube detection, a 3D deformable RoI pooling layer designed for learning the flexible, deformable geometry of the constituent action tubes, and a scene graph constructed by considering all parts as nodes and connecting them based on different semantics. In a second approach evolving from the first, we propose a hybrid graph neural network that combines attention applied to a graph encoding of the local (short-term) dynamic scene with a temporal graph modelling the overall long-duration activity. Our contribution is threefold: i) a feature extraction technique; ii) a method for constructing a local scene graph followed by graph attention, and iii) a graph for temporally connecting all the local dynamic scene graphs. Finally, the last part of the thesis is about presenting a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community. We also propose to formulate the continual semi-supervised learning problem as a latent-variable
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