1,248 research outputs found
3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack
With the maturity of depth sensors, the vulnerability of 3D point cloud
models has received increasing attention in various applications such as
autonomous driving and robot navigation. Previous 3D adversarial attackers
either follow the white-box setting to iteratively update the coordinate
perturbations based on gradients, or utilize the output model logits to
estimate noisy gradients in the black-box setting. However, these attack
methods are hard to be deployed in real-world scenarios since realistic 3D
applications will not share any model details to users. Therefore, we explore a
more challenging yet practical 3D attack setting, \textit{i.e.}, attacking
point clouds with black-box hard labels, in which the attacker can only have
access to the prediction label of the input. To tackle this setting, we propose
a novel 3D attack method, termed \textbf{3D} \textbf{H}ard-label
att\textbf{acker} (\textbf{3DHacker}), based on the developed decision boundary
algorithm to generate adversarial samples solely with the knowledge of class
labels. Specifically, to construct the class-aware model decision boundary,
3DHacker first randomly fuses two point clouds of different classes in the
spectral domain to craft their intermediate sample with high imperceptibility,
then projects it onto the decision boundary via binary search. To restrict the
final perturbation size, 3DHacker further introduces an iterative optimization
strategy to move the intermediate sample along the decision boundary for
generating adversarial point clouds with smallest trivial perturbations.
Extensive evaluations show that, even in the challenging hard-label setting,
3DHacker still competitively outperforms existing 3D attacks regarding the
attack performance as well as adversary quality.Comment: Accepted by ICCV 202
AI-based design methodologies for hot form quench (HFQ®)
This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits.
To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality.
The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces
Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning
Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and
direct surgical procedures, and to track the development of bone-related diseases. This often
involves radiologists who have to annotate bones manually or in a semi-automatic way, which is
a time consuming task. Their annotation workload can be reduced by automated segmentation
and detection of individual bones. This automation of distinct bone segmentation not only has
the potential to accelerate current workflows but also opens up new possibilities for processing
and presenting medical data for planning, navigation, and education.
In this thesis, we explored the use of deep learning for automating the segmentation of all
individual bones within an upper-body CT scan. To do so, we had to find a network architec-
ture that provides a good trade-off between the problem’s high computational demands and the
results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out
to eliminate the most prevalent types of error. To do so, we introduced an novel method called
binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin-
guishing bone from non-bone is conducted separately from identifying the individual bones.
Both predictions are then merged, which leads to superior results. Another type of error is tack-
led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger
fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input
into the network while keeping the growth of additional pixels in check.
Overall, we present a deep-learning-based method that reliably segments most of the over
one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter
quickly enough to be used in interactive software. Our algorithm has been included in our
groups virtual reality medical image visualisation software SpectoVR with the plan to be used
as one of the puzzle piece in surgical planning and navigation, as well as in the education of
future doctors
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Cerebrovascular dysfunction in cerebral small vessel disease
INTRODUCTION:
Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD.
METHODS:
Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs.
RESULTS:
Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion.
DISCUSSION:
Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
Transformer neural networks show promising capabilities, in particular for
uses in materials analysis, design and manufacturing, including their capacity
to work effectively with both human language, symbols, code, and numerical
data. Here we explore the use of large language models (LLMs) as a tool that
can support engineering analysis of materials, applied to retrieving key
information about subject areas, developing research hypotheses, discovery of
mechanistic relationships across disparate areas of knowledge, and writing and
executing simulation codes for active knowledge generation based on physical
ground truths. When used as sets of AI agents with specific features,
capabilities, and instructions, LLMs can provide powerful problem solution
strategies for applications in analysis and design problems. Our experiments
focus on using a fine-tuned model, MechGPT, developed based on training data in
the mechanics of materials domain. We first affirm how finetuning endows LLMs
with reasonable understanding of domain knowledge. However, when queried
outside the context of learned matter, LLMs can have difficulty to recall
correct information. We show how this can be addressed using
retrieval-augmented Ontological Knowledge Graph strategies that discern how the
model understands what concepts are important and how they are related.
Illustrated for a use case of relating distinct areas of knowledge - here,
music and proteins - such strategies can also provide an interpretable graph
structure with rich information at the node, edge and subgraph level. We
discuss nonlinear sampling strategies and agent-based modeling applied to
complex question answering, code generation and execution in the context of
automated force field development from actively learned Density Functional
Theory (DFT) modeling, and data analysis
Artificial Intelligence for the Edge Computing Paradigm.
With modern technologies moving towards the internet of things where seemingly every financial, private, commercial and medical transaction being carried out by portable and intelligent devices; Machine Learning has found its way into every smart device and application possible. However, Machine Learning cannot be used on the edge directly due to the limited capabilities of small and battery-powered modules. Therefore, this thesis aims to provide light-weight automated Machine Learning models which are applied on a standard edge device, the Raspberry Pi, where one framework aims to limit parameter tuning while automating feature extraction and a second which can perform Machine Learning classification on the edge traditionally, and can be used additionally for image-based explainable Artificial Intelligence. Also, a commercial Artificial Intelligence software have been ported to work in a client/server setups on the Raspberry Pi board where it was incorporated in all of the Machine Learning frameworks which will be presented in this thesis. This dissertation also introduces multiple algorithms that can convert images into Time-series for classification and explainability but also introduces novel Time-series feature extraction algorithms that are applied to biomedical data while introducing the concept of the Activation Engine, which is a post-processing block that tunes Neural Networks without the need of particular experience in Machine Leaning. Also, a tree-based method for multiclass classification has been introduced which outperforms the One-to-Many approach while being less complex that the One-to-One method.\par
The results presented in this thesis exhibit high accuracy when compared with the literature, while remaining efficient in terms of power consumption and the time of inference. Additionally the concepts, methods or algorithms that were introduced are particularly novel technically, where they include:
• Feature extraction of professionally annotated, and poorly annotated time-series.
• The introduction of the Activation Engine post-processing block.
• A model for global image explainability with inference on the edge.
• A tree-based algorithm for multiclass classification
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