1,016 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Sound Event Detection by Exploring Audio Sequence Modelling

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    Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

    Low- and high-resource opinion summarization

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    Customer reviews play a vital role in the online purchasing decisions we make. The reviews express user opinions that are useful for setting realistic expectations and uncovering important details about products. However, some products receive hundreds or even thousands of reviews, making them time-consuming to read. Moreover, many reviews contain uninformative content, such as irrelevant personal experiences. Automatic summarization offers an alternative – short text summaries capturing the essential information expressed in reviews. Automatically produced summaries can reflect overall or particular opinions and be tailored to user preferences. Besides being presented on major e-commerce platforms, home assistants can also vocalize them. This approach can improve user satisfaction by assisting in making faster and better decisions. Modern summarization approaches are based on neural networks, often requiring thousands of annotated samples for training. However, human-written summaries for products are expensive to produce because annotators need to read many reviews. This has led to annotated data scarcity where only a few datasets are available. Data scarcity is the central theme of our works, and we propose a number of approaches to alleviate the problem. The thesis consists of two parts where we discuss low- and high-resource data settings. In the first part, we propose self-supervised learning methods applied to customer reviews and few-shot methods for learning from small annotated datasets. Customer reviews without summaries are available in large quantities, contain a breadth of in-domain specifics, and provide a powerful training signal. We show that reviews can be used for learning summarizers via a self-supervised objective. Further, we address two main challenges associated with learning from small annotated datasets. First, large models rapidly overfit on small datasets leading to poor generalization. Second, it is not possible to learn a wide range of in-domain specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We address the first challenge by explicitly modeling summary properties (e.g., content coverage and sentiment alignment). Furthermore, we leverage small modules – adapters – that are more robust to overfitting. As we show, despite their size, these modules can be used to store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and ‘resolution.’ This task is harder to learn, and we present a few-shot method for training a query-based summarizer on small annotated datasets. In the second part, we focus on the high-resource setting and present a large dataset with summaries collected from various online resources. The dataset has more than 33,000 humanwritten summaries, where each is linked up to thousands of reviews. This, however, makes it challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To address this problem, we propose selecting small subsets of informative reviews. Only these subsets are encoded by the deep encoder and subsequently summarized. We show that the selector and summarizer can be trained end-to-end via amortized inference and policy gradient methods

    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

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Undergraduate Catalog of Studies, 2022-2023

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    Comment sections and their role in a democratic society

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    Kommentarfelt lar lesere uttrykke seg offentlig innen en rekke temaer, gjør det mulig med direkte tilbakemelding til journalister og redaktører, og de kan potensielt legge til rette for en demokratisk verdifull offentlig debatt. Til tross for dette er kommentarfelt blitt kritisert på grunn av uhemmet atferd, usiviliserte og uhøflige kommentarer, samt politisk polariserende innhold. I den offentlige debatten blir kommentarfelt ofte beskrevet som problematiske, og det meste av forskning relatert til kommentarfelt setter søkelys på slik uhemmet atferd. Denne avhandlingen utforsker rollen kommentarfelt har i et demokratisk samfunn. Når man ser på kommentarfelt gjennom rammeverk basert på demokratiske teorier kan det virke som at kommentarfelt ikke lever opp til demokratiske standarder. Kommentarfelt har en tendens til å bli dømt basert på standardene til deliberative demokratiske teorier. Slike teorier legger vekt på åpen deltakelse og verdsetter beslutningstaking basert på rimelig argumentasjon. Å benytte slike teorier kan derimot være problematisk. Det vanskelig å bruke deliberative teorier som et rammeverk fordi kommentarfelt ikke har et spesifikt punkt der en beslutning blir tatt på bakgrunn av den foregående diskusjonen. En diskusjon i et kommentarfelt tar slutt når alle deltakere har sagt det de skulle si, slik at debatten dør på egen hånd uten at noen beslutninger har blitt tatt. Et annet sett med demokratiske teorier som kanskje passer kommentarfelt bedre, som for eksempel participatory liberal theory og agonistic democracy, fokuserer mer på deltakelse som viktig for demokratier. Kommentarfelt gjør i første øyekast deltakelse i offentlige debatter enklere. Men slike teorier fokuserer også på gjensidig respekt som et grunnlag for offentlig debatt, noe kommentarfelt er kritisert for å mangle. Det kan være at den beste teorien for å forstå kommentarfelts rolle i et demokratisk samfunn er ideen om post-demokrati, der kommentarfelt kan ha en rolle som et anti-establishment, ikke-profesjonelt forum på profesjonelle nyhetsnettsteder. I denne avhandlingen er tre interessefelt blitt forsket på gjennom tre artikler: effekten av anonymitet på antisosial atferd, anklagelser av trolling, og mediekritikk i kommentarfelt. Avhandlingen presenterer disse forskningsprosjektene og diskuterer kommentarfelts rolle I et demokratisk samfunn, samt de metodologiske utfordringene som følger med når man forsker på kommentarfelt. Siden antisosial atferd blir diskutert mye og anonymitet ofte blir brukt for å forklare slik atferd, ble en studie gjennomført der anonyme og ikke-anonyme kommentarer fra samme plattformer ble analysert. Anonymitet hadde en liten, men statistisk signifikant effekt på antisosial atferd. Avhandlingen har også funnet at anklagelser av trolling ofte var politisk motivert og brukt for å se bort fra andres argumenter man ikke var enige i, og at disse anklagelsene stort sett ble ignorert av andre deltakere og de som ble anklaget. Til slutt utforsker og kategoriserer avhandlingen kritikk av media i kommentarfelt. Tre typer kritikker blitt identifisert: kritikk av fokus, kvalitet og integritet. En andre dimensjon, målet for kritikk, ble også identifisert: journalister, nyhetsorganisasjoner, og media. Denne avhandlingen konkluderer med at kommentarfelts rolle i et demokratisk samfunn er utfordrende, og at det største hinderet for at kommentarfelt skal spille ne viktig positiv rolle er antisosial atferd. Men kommentarfelt har stort potensial til å kunne bli en demokratisk verdifull form for offentlige ytringer som får folk til å besøke nyhetsnettsteder, det er en plattform der folks meninger blir utfordret, og en plattform for konstruktiv kritikk av media.Newspaper comment sections provide readers with a public platform to voice their opinion on a wide range of topics, provide a direct line of feedback for journalists and editors, and have the potential of facilitating a democratically valuable public debate. However, comment sections have come under scrutiny for the prevalence of disinhibited behavior, uncivil and impolite comments, as well as politically polarizing content. In the public debate, comment sections are often described as problematic, and most research that relates to comment sections, tend to focus on incivility and impoliteness. This thesis explores the role of comment sections in a democratic society. When considering comment sections through frameworks based on democratic theories, comment sections appear to fail to live up to democratically valuable standards. Comment sections tend to be judged by the standards of theories such as deliberative democracy and discursive, which emphasize open participation and places high value on making decisions based on reasonable argumentation. Using these theories, however, might be problematic. It is difficult to use these theories as a framework for discussing comment sections, because comment sections do not have a set point when a decision is made based on a preceding discussion. A discussion in a comment section only ends when all commenters have said what they wanted to say, at which point the debate dies down on its own without any decision having been made. Comment sections might be more suited within democratic frameworks that focus more on participation, such as participatory liberal theory and agonistic democracy. Participatory theories focus more on the participation aspect of democracy, and comment sections do, at least on first glance, make participation in the public debate easier. However, these theories also emphasize mutual respect as a basis for public discussion, something that comment sections are criticized for lacking. In the end, it might be that the best theory to understand the role of comment sections in a democratic society is the idea of the post-democracy, in which comment sections may serve a role as an anti-establishment, non-professional forum on professional, establishment news sites. For this thesis, three topics of interest have been investigated in three papers: the effect of anonymity on toxicity, accusations of trolling, and media criticism in comment sections. This thesis presents these research projects and discusses the role of comment sections in a democratic society, as well as the methodological challenges when researching comment sections. As toxicity is a much-debated topic, and anonymity is often used to explain such behavior, a study was devised where anonymous and non-anonymous comments from the same platform were analyzed, showing that anonymity has a small, but statistically significant effect on toxicity. This thesis also found that accusations of trolling are often politically motivated and used to dismiss opposing arguments and that these accusations were mostly ignored by other debaters and the accused. Finally, this thesis explores and categorizes criticism of the media found in comment sections. Three kinds of media criticism were identified: criticism of focus, quality and of integrity. A second dimension, target of criticism, was also identified: journalists, news organizations, and the media. The thesis concludes that the role of comment sections in a democratic society is challenging and that the greatest obstacle for comment sections playing an important, positive role is the prevalence of toxic disinhibition. There is, however, great potential for comment sections being a democratically valuable forum for public expression that incentivizes people to engage with the news media, where people have their opinions challenged and a platform for constructive criticism of the media.Doktorgradsavhandlin

    Toward Efficient and Robust Computer Vision for Large-Scale Edge Applications

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    The past decade has been witnessing remarkable advancements in computer vision and deep learning algorithms, ushering in a transformative wave of large-scale edge applications across various industries. These image processing methods, however, still encounter numerous challenges when it comes to meeting real-world demands, especially in terms of accuracy and latency at scale. Indeed, striking a balance among efficiency, robustness, and scalability remains a common obstacle. This dissertation investigates these issues in the context of different computer vision tasks, including image classification, semantic segmentation, depth estimation, and object detection. We introduce novel solutions, focusing on utilizing adjustable neural networks, joint multi-task architecture search, and generalized supervision interpolation. The first obstacle revolves around the ability to trade off between speed and accuracy in convolutional neural networks (CNNs) during inference on resource-constrained platforms. Despite their progress, CNNs are typically monolithic at runtime, which can present practical difficulties since computational budgets may vary over time. To address this, we introduce Any-Width Network, an adjustable-width CNN architecture that utilizes a novel Triangular Convolution module to enable fine-grained control over speed and accuracy during inference. The second challenge focuses on the computationally demanding nature of dense prediction tasks such as semantic segmentation and depth estimation. This issue becomes especially problematic for edge platforms with limited resources. To tackle this, we propose a novel and scalable framework named EDNAS. EDNAS leverages the synergistic relationship between Multi-Task Learning and hardware-aware Neural Architecture Search to significantly enhance on-device speed and accuracy of dense predictions. Finally, to improve the robustness of object detection, we introduce a novel data mixing augmentation. While mixing techniques such as Mixup have proven successful in image classification, their application to object detection is non-trivial due to spatial misalignment, foreground/background distinction, and instance multiplicity. To address these issues, we propose a generalized data mixing principle, Supervision Interpolation, and its simple yet effective implementation, LossMix. By addressing these challenges, this dissertation aims to facilitate better efficiency, accuracy, and scalability of computer vision and deep learning algorithms and contribute to the advancement of large-scale edge applications across different domains.Doctor of Philosoph

    Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

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    The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks
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