7,108 research outputs found
Mobile Device Background Sensors: Authentication vs Privacy
The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process
Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements
In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin
Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis
In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging
The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI’s performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform
Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches
Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people.
Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy.
The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech.
In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection
- …