Point Transformer-Based Human Activity Recognition Using High-Dimensional Radar Point Clouds

Radar-based Human Activity Recognition(HAR) is considered by using snapshots of point clouds. Such point cloudsinterpret 2D images generated by an mm-wave FMCW MIMO radar enriched byincluding Doppler and temporal information. We use the similarity between suchradar data representation and the core of the self-attention concept inartificial...

On the Strengths of Pure Evolutionary Algorithms in Generating Adversarial Examples

Deep learning (DL) models are known to be highly accurate, yet vulnerable to adversarial examples. While earlier research focused on generating adversarial examples using whitebox strategies, later research focused on black-box strategies, as models often are not accessible to external attackers. Prior studies showed that black-box approaches...

Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries

Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving considering effort in labelling code with helpful summaries....

Can We Empower Attentive E-reading with a Social Robot? An Introductory Study with a Novel Multimodal Dataset and Deep Learning Approaches

Reading on digital devices has become more commonplace, while it often poses challenges to learners' attention. In this study, we hypothesized that allowing learners to reflect on their reading phases with an empathic social robot companion might enhance learners' attention in e-reading. To verify our assumption, we collected a novel dataset ...

Continuous People Crowd Monitoring defined as a Regression Problem using Radar Networks

Radar-based human activity recognition in crowded environments using regression approaches is addressed. Whereas previous research has focused on single activities and subjects, the problem of continuous activity recognition involving up to five individuals moving in arbitrary directions in an indoor area is introduced. To treat the problem, a...

Radar-only Instantaneous Ego-motion Estimation Using Neural Networks

The problem of 2D instantaneous ego-motion estimation for vehicles equipped with automotive radars is studied. To leverage multi-dimensional radar point clouds and exploit point features automatically, without human engineering, a novel approach is proposed that transforms ego-motion estimation into a weighted least squares (wLSQ) problem using...

Privacy-Friendly De-Authentication with BLUFADE: Blurred Face Detection

Ideally, secure user sessions should start and end with authentication and de-Authentication phases, respectively. While the user must pass the former to start a secure session, the latter's importance is often ignored or underestimated. Dangling or unattended sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, the...

Deep learning based prediction of fibrous microstructure permeability

Knowledge of permeability of fibrous microstructures is crucial for predicting the mold fill times and resin flow path in composite manufacturing. Herein we report a method to rapidly predict the permeability of 3D fibrous microstructures. Our method relies on predicting the permeability of 2D cross-sections via deep neural networks and...

Infrared thermal defect identification and reconstruction of artworks using a spatiotemporal deep neural network

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) is an interesting concept since surface and subsurface faults can be...

Balanced Dual-Mask Protection Scheme for GIFT Cipher Against Power Attacks

Currently NIST is working towards the standardization of lightweight cryptography (LWC). Although the cryptanalytic strength of LWC is currently under deep scrutiny, the LWC implementation security has not been yet widely explored. GIFT block cipher is the main building block of many of the LWC NIST candidates and therefore has the potential to...

Data variation-aware medical image segmentation

Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowledged to still be a problem. One way to automatically overcome this is to capture and exploit this variation explicitly. Here, we propose an approach that improves...

Evolutionary neural cascade search across supernetworks

To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery of task-specific network architectures. Modern NAS approaches leverage super-networks whose subnetworks encode candidate neural network architectures. These...

On the Evaluation of Deep Learning-Based Side-Channel Analysis

Deep learning-based side-channel analysis is rapidly positioning itself as a de-facto standard for the most powerful profiling side-channel analysis.The results from the last few years show that deep learning techniques can efficiently break targets that are even protected with countermeasures. While there are constant improvements in making...

WHOSe Heritage: Classification of UNESCO World Heritage Statements of "outstanding Universal Value" with Soft Labels

The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL, and yet a complex task, even for experts, since the selection criteria of OUV are not mutually...

Model-to-Image Registration via Deep Learning towards Image-Guided Endovascular Interventions

Cardiologists highlight the need for an intra-operative 3D visualization to assist interventions. The intra-operative 2D X-ray/Digital Subtraction Angiography (DSA) images in the standard clinical workflow limit cardiologists’ views significantly. Compared with image-to-image registration, model-to-image registration is an essential approach...

Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems

Recently, reconstruction-based anomaly detection was proposed as an effective technique to detect attacks in dynamic industrial control networks. Unlike classical network anomaly detectors that observe the network traffic, reconstruction-based detectors operate on the measured sensor data, leveraging physical process models learned a priori....

Interactive Learning with Corrective Feedback for Policies Based on Deep Neural Networks

Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN...