11 research outputs found

    Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities

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    There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through two public sector procurement checklists, identifying what we can do now, what we should be able to do with technical innovation in AI, and what requirements necessitate a more interdisciplinary approach

    Predicting Humans’ Identity and Mental Load from EEG: Performed by AI

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    EEG-based brain machine/computer interfaces (BMIs/BCIs) have a wide range of clinical and non-clinical applications. Mental workload (MW) classification, emotion recognition, motor imagery, seizure detection, and sleep stage scoring are among the active BCI research areas. One of the relatively new BCI area is EEG-based human subject recognition (i.e., EEG biometric). There still exist several challenges that need to be addressed to design a successful EEG-based biometric model applicable for real-world environments. First, there is a need for a protocol that can elicit the individual dependent EEG responses in a short period of time. A classification algorithm with high generalization power is also required to deal with the EEG signals classification task. The latter is a common challenge for all EEG-based BCI paradigms; given the non-stationary nature of the EEG signals and the small size of the EEG datasets. In addition, to building a stable EEG biometric model, the effects of human mental states (e.g., emotion, mental load) on the model performance needs to be carefully examined. In this thesis, a new protocol for the area of the EEG biometric has been proposed. The proposed protocol called “(the) N-back task” is based on the human working memory and the experimental results obtained in this thesis prove that the EEG signals elicited by the N-back task contain subject specific features, even for very short time intervals. It has also been shown that three load levels of the typical N-back task are all capable of evoking subject specific EEG features. As a result, the N-back task can be used as a protocol having more than one mode (i.e, cancelable protocol) that comes with added security benefits. The EEG signals evoked by the N-back task have been used to train a compact convolutional neural network called the EEGNet. A configuration of the EEGNet having 16 temporal and 2 spatial filters has reached an identification accuracy of approximately 97% using data instances as short as 1.1s for a pool of 26 subjects. To further improve the accuracy, a novel ensemble classifier has been designed in this thesis. The principle underlying the proposed ensemble is the “division and exclusion” of the EEG channels guided by scalp locations. The ensemble classifier has (statistically significantly) improved the subject recognition rate from 97% to 99%. Performance of the proposed ensemble model has also been assessed in the EEG-based MW classification paradigm. The ensemble classifier outperformed the single EEGNet as well as a state-of-the-art classifier called WLnet in the challenging scenario of the subject-independent (cross-subject) MW classification. The results suggest that the ensemble structure proposed in this thesis can generalize to different BCI paradigms. Finally, effects of the mental workload on the performance of the EEG-based subject authentication models have been thoroughly explored in this thesis. The obtained results affirm that MW of the genuine and impostor subjects at the train and test phases have significant effects on both false negative rate (FNR) and false positive rate (FPR) of an authentication system. Different subjects have also shown different clusters of authentication behaviors when affected by the MW changes. This finding establishes the importance of the human’s mental load in the design of real-world EEG authentication systems and introduces a new investigation line for the EEG biometric community

    Predictive maintenance of electrical grid assets: internship at EDP Distribuição - Energia S.A

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    Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis report will describe the activities developed during an internship at EDP Distribuição, focusing on a Predictive Maintenance analytics project directed at high voltage electrical grid assets including Overhead Lines, Power Transformers and Circuit Breakers. The project’s main goal is to support EDP’s asset management processes by improving maintenance and investing planning. The project’s main deliverables are the Probability of Failure metric that forecast asset failures 15 days ahead of time, estimated through supervised machine learning models; the Health Index metric that indicates asset’s current state and condition, implemented though the Ofgem methodology; and two asset management dashboards. The project was implemented by an external service provider, a consultant company, and during the internship it was possible to integrate the team, and participate in the development activities

    Optimisation of the Rugby Wheelchair for Performance

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    Equipment design in para-sport has a substantial impact on athlete performance. Subsequently, wheelchair designs have progressed to reflect the requirements of their sports; for wheelchair rugby, this has resulted in features including reinforced frames to withstand the frequent high impacts and cambered wheels for improved agility and stability. Whilst these aspects of wheelchair design have advanced, there is currently no accepted method for optimising an individual’s wheelchair configuration (e.g., setting of seat height/seat angle); instead, players rely on their previous experience and support staff in trial-and-error approaches to prescribing set-ups. This is likely due to a number of factors, including: the range of impairment types and severities in the sport, hence optimal set-ups differing across players; difficulty in assessing on-court performance and propulsion kinematics; limited knowledge of the effects of set-up parameters on key performance and propulsion factors; and the substantial time and cost associated with new chair prescriptions. To address this issue, this research aims to improve the knowledge regarding the effect configuration parameters have on performance and propulsion in wheelchair rugby. To achieve this, an improved understanding of current player set-ups and their propulsion approaches is required. Large participant groups (n=16 and 25, for set-up and propulsion analysis respectively) allowed for statistical assessments based on classification groups (high-, mid-, and low-point groups). Significant differences were found in both set-up and propulsion approaches across classifications. The majority of these differences reflect the levels of the player’s activity limitation (i.e., high-point players with greater trunk range of motion used flatter seat angles, and contacted the wheel closer to top dead centre than low-point players). Additionally, a potential trend towards increasing release angles and greater peak accelerations was identified. More detailed individual assessments of propulsion were also performed that revealed variations in intra-stroke acceleration profiles of three players. This information can aid in wheelchair prescription by identifying regions of strength for an individual, with this then emphasised by the wheelchair set-up. To assess the effect of set-up parameters on performance and propulsion measures, a robust design approach using an adjustable wheelchair was implemented with six elite players. This approach required reduced amounts of field testing whilst maintaining the ability to identify the effect of the specific settings of seat height, seat depth, seat angle, and tyre pressure. Half the players reported a blinded preference for a recommended set-up following this testing, while remaining players reported a preference based on ‘comfort’ despite similar results. Finally, a linkage model and regression approach were developed that accounted for individual anthropometrics, propulsion approach, and wheelchair set-up and successfully predicted a performance measure for some players. Overall, this research has improved the knowledge surrounding the effect of wheelchair rugby set-up parameters on performance and propulsion at both group and individual levels. Optimisation of wheelchair set-up should occur at an individual level and consider functional abilities and on-court role; approaches such as the robust design and modelling methods presented in this thesis improve the ability to achieve this in practise.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

    Improving Efficiency and Generalization of Visual Recognition

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    Deep Neural Networks (DNNs) are heavy in terms of their number of parameters and computational cost. This leads to two major challenges: first, training and deployment of deep networks are expensive; second, without tremendous annotated training data, which are very costly to obtain, DNNs easily suffer over-fitting and have poor generalization. We propose approaches to these two challenges in the context of specific computer vision problems to improve their efficiency and generalization. First, we study network pruning using neuron importance score propagation. To reduce the significant redundancy in DNNs, we formulate network pruning as a binary integer optimization problem which minimizes the reconstruction errors on the final responses produced by the network, and derive a closed-form solution to it for pruning neurons in earlier layers. Based on our theoretical analysis, we propose the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network, then prune neurons in the entire networks jointly. Second, we study visual relationship detection (VRD) with linguistic knowledge distillation. Since the semantic space of visual relationships is huge and training data is limited, especially for long-tail relationships that have few instances, detecting visual relationships from images is a challenging problem. To improve the predictive capability, especially generalization on unseen relationships, we utilize knowledge of linguistic statistics obtained from both training annotations (internal knowledge) and publicly available text, e.g., Wikipedia (external knowledge) to regularize visual model learning. Third, we study the role of context selection in object detection. We investigate the reasons why context in object detection has limited utility by isolating and evaluating the predictive power of different context cues under ideal conditions in which context provided by an oracle. Based on this study, we propose a region-based context re-scoring method with dynamic context selection to remove noise and emphasize informative context. Fourth, we study the efficient relevant motion event detection for large-scale home surveillance videos. To detect motion events of objects-of-interest from large scale home surveillance videos, traditional methods based on object detection and tracking are extremely slow and require expensive GPU devices. To dramatically speedup relevant motion event detection and improve its performance, we propose a novel network for relevant motion event detection, ReMotENet, which is a unified, end-to-end data-driven method using spatial-temporal attention-based 3D ConvNets to jointly model the appearance and motion of objects-of-interest in a video. In the last part, we address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We introduce a representation of the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training set to unseen layouts in the test set. This Layout-Induced Video Representation (LIVR) abstracts away low-level appearance variance and encodes geometric and topological relationships of places in a specific scene layout. LIVR partitions the semantic features of a video clip into different places to force the network to learn place-based feature descriptions; to predict the confidence of each action, LIVR aggregates features from the place associated with an action and its adjacent places on the scene layout. We introduce the Agent-in-Place Action dataset to show that our method allows neural network models to generalize significantly better to unseen scenes

    Characterization, prevalence, and risk factors of spontaneous and experimental atherosclerosis and vascular imaging in psittaciformes

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    Atherosclerosis is a degenerative and inflammatory vascular disease characterized by the accumulation of inflammatory cells, lipids, calcium, and formation of large fibrofatty lesions within the intima of arteries resulting in the disorganization of the arterial wall and stenosis of the lumen. Despite the importance of atherosclerosis in psittacine cardiology, there are few pathologic, clinical, and experimental investigations in psittaciformes. In order to expand the knowledge on psittacine atherosclerosis and provide fundamental observational information for future research, a series of studies was conducted on psittaciformes: 1) psittacine atherosclerotic lesions were characterized and categorized based on histopathology, histochemistry, transmission (TEM), and scanning electron microscopy (SEM) of 63 arterial samples, 2) the prevalence of clinically significant atherosclerotic lesions and the influence of several epidemiological variables were investigated in a multi-center case-control study on 7683 psittaciformes, 3) a diet-induced experimental model of atherosclerosis was developed and characterized in Quaker parrots (Myiopsitta monachus), and 4) a computed-tomography angiographic (CTA) protocol was developed and standardized to image the arterial lumen in Hispaniolan Amazon parrots (Amazona ventralis). Seven lesion types could be described in psittaciformes, which were similar to the human classification system. Digital image analysis, TEM, and SEM helped to further describe the lesions and refine the classification system. Atherosclerosis prevalence significantly increased with age, female sex, and the genera Psittacus, Amazona, and Nymphicus. Mild associations with reproductive, hepatic diseases, and myocardial fibrosis were also evidenced. Experimental induction of atherosclerosis with dietary 1% cholesterol lead to significant lesions within 2 months in Quaker parrots. An increase in arterial and plasma cholesterol and LDL was also documented. Reference limits for arterial luminal diameter of Hispaniolan Amazon parrots were determined by CTA and measurements revealed high intraobserver and interobserver agreement. In conclusion, psittacine atherosclerotic lesions displayed distinctive features that allowed the development of an effective classification system. The prevalence of advanced lesions (type IV-VI) was associated with several risk factors: age, female sex, and three psittacine genera. The Quaker parrot was found to be a suitable experimental model for psittacine atherosclerosis and dyslipidemia. Finally CTA was determined to be safe, reliable, and of potential diagnostic value in parrots for diagnosing stenotic atherosclerotic lesions

    Psychopathology in children with intellectual disability

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    Psychopathology in children with intellectual disability

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    Psychopathology in Children with Intellectual Disability: Assessment, prevalence and predictive factors

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    __Abstract__ This study’s main focus is psychopathology in children and adolescents with intellectual disability (ID). The main topics addressed in this study are the assessment of psychopathology in children (also including adolescents) with ID, the prevalence and impact of psychopathology in children with ID, and associated risk factors. The present study is an epidemiological study. In short, epidemiological studies are concerned with the study of patterns of disease occurrence in human populations, and with the factors that influence those patterns. Epidemiological research is empirical by nature, involves quantification of relevant factors, is probabilistic, and uses the method of comparison as a basic tool (Verhulst, 1995). The number of studies addressing the epidemiology of psychopathology in children with ID is limited, though it has increased in the last decade. Few systematic studies on the relationship between ID and psychopathology in children exist. Theoretical considerations and empirical findings suggest that children with ID are at higher risk than children without ID for developing psychopathology. Further, several other issues hamper our understanding of the subject, such as the use of different definitions for both ID and psychopathology, the lack of standardized assessment procedures, and the use of not so representative samples. In this chapter we will discuss some of the major issues in this research-field. Further, we will account for the choices made in this study in an effort to provide good quality data on the epidemiology of psychopathology in children with ID
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