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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
Sign Language Translation from Instructional Videos
The advances in automatic sign language translation (SLT) to spoken languages
have been mostly benchmarked with datasets of limited size and restricted
domains. Our work advances the state of the art by providing the first baseline
results on How2Sign, a large and broad dataset.
We train a Transformer over I3D video features, using the reduced BLEU as a
reference metric for validation, instead of the widely used BLEU score. We
report a result of 8.03 on the BLEU score, and publish the first open-source
implementation of its kind to promote further advances.Comment: Paper accepted at WiCV @CVPR2
TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation
Multi-modal fusion of sensors is a commonly used approach to enhance the
performance of odometry estimation, which is also a fundamental module for
mobile robots. However, the question of \textit{how to perform fusion among
different modalities in a supervised sensor fusion odometry estimation task?}
is still one of challenging issues remains. Some simple operations, such as
element-wise summation and concatenation, are not capable of assigning adaptive
attentional weights to incorporate different modalities efficiently, which make
it difficult to achieve competitive odometry results. Recently, the Transformer
architecture has shown potential for multi-modal fusion tasks, particularly in
the domains of vision with language. In this work, we propose an end-to-end
supervised Transformer-based LiDAR-Inertial fusion framework (namely
TransFusionOdom) for odometry estimation. The multi-attention fusion module
demonstrates different fusion approaches for homogeneous and heterogeneous
modalities to address the overfitting problem that can arise from blindly
increasing the complexity of the model. Additionally, to interpret the learning
process of the Transformer-based multi-modal interactions, a general
visualization approach is introduced to illustrate the interactions between
modalities. Moreover, exhaustive ablation studies evaluate different
multi-modal fusion strategies to verify the performance of the proposed fusion
strategy. A synthetic multi-modal dataset is made public to validate the
generalization ability of the proposed fusion strategy, which also works for
other combinations of different modalities. The quantitative and qualitative
odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom
could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work
has been submitted to the IEEE for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images
BackgroundAccurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image.MethodsIn particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.ResultsThe proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.ConclusionOur results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
Spurious correlations that degrade model generalization or lead the model to
be right for the wrong reasons are one of the main robustness concerns for
real-world deployments. However, mitigating these correlations during
pre-training for large-scale models can be costly and impractical, particularly
for those without access to high-performance computing resources. This paper
proposes a novel approach to address spurious correlations during fine-tuning
for a given domain of interest. With a focus on multi-modal models (e.g.,
CLIP), the proposed method leverages different modalities in these models to
detect and explicitly set apart spurious attributes from the affected class,
achieved through a multi-modal contrastive loss function that expresses
spurious relationships through language. Our experimental results and in-depth
visualizations on CLIP show that such an intervention can effectively i)
improve the model's accuracy when spurious attributes are not present, and ii)
directs the model's activation maps towards the actual class rather than the
spurious attribute when present. In particular, on the Waterbirds dataset, our
algorithm achieved a worst-group accuracy 23% higher than ERM on CLIP with a
ResNet-50 backbone, and 32% higher on CLIP with a ViT backbone, while
maintaining the same average accuracy as ERM
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
EEG-based recognition of activities and states involves the use of prior
neuroscience knowledge to generate quantitative EEG features, which may limit
BCI performance. Although neural network-based methods can effectively extract
features, they often encounter issues such as poor generalization across
datasets, high predicting volatility, and low model interpretability. Hence, we
propose a novel lightweight multi-dimensional attention network, called
LMDA-Net. By incorporating two novel attention modules designed specifically
for EEG signals, the channel attention module and the depth attention module,
LMDA-Net can effectively integrate features from multiple dimensions, resulting
in improved classification performance across various BCI tasks. LMDA-Net was
evaluated on four high-impact public datasets, including motor imagery (MI) and
P300-Speller paradigms, and was compared with other representative models. The
experimental results demonstrate that LMDA-Net outperforms other representative
methods in terms of classification accuracy and predicting volatility,
achieving the highest accuracy in all datasets within 300 training epochs.
Ablation experiments further confirm the effectiveness of the channel attention
module and the depth attention module. To facilitate an in-depth understanding
of the features extracted by LMDA-Net, we propose class-specific neural network
feature interpretability algorithms that are suitable for event-related
potentials (ERPs) and event-related desynchronization/synchronization
(ERD/ERS). By mapping the output of the specific layer of LMDA-Net to the time
or spatial domain through class activation maps, the resulting feature
visualizations can provide interpretable analysis and establish connections
with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows
great potential as a general online decoding model for various EEG tasks.Comment: 20 pages, 7 Figure
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