Miracle-dz/UniFAR-FTUs huggingface dataset:
Absolute Security in High-Frequency Wireless Links

Security against eavesdropping is one of the key concerns in the design of any communication system. Many common considerations of the security of a wireless communication channel rely on comparing the signal level measured by Bob (the intended receiver) to that accessible to Eve (an eavesdropper). Frameworks such as Wyner's wiretap model ensure the security of a link, in an average sense, when Bob's signal-to-noise ratio exceeds Eve's. Unfortunately, because these guarantees rely on statistical assumptions about noise, Eve can still occasionally succeed in decoding information. The goal of achieving exactly zero probability of intercept over an engineered region of the broadcast sector, which we term absolute security, remains elusive. Here, we describe the first architecture for a wireless link which provides absolute security. Our approach relies on the inherent properties of broadband and high-gain antennas, and is therefore ideally suited for implementation in millimeter-wave and terahertz wireless systems, where such antennas will generally be employed. We exploit spatial minima of the antenna pattern at different frequencies, the union of which defines a wide region where Eve is guaranteed to fail regardless of her computational capabilities, and regardless of the noise in the channels. Unlike conventional zero-forcing beam forming methods, we show that, for realistic assumptions about the antenna configuration and power budget, this absolute security guarantee can be achieved over most possible eavesdropper locations. Since we use relatively simple frequency-multiplexed coding, together with the underlying physics of a diffracting aperture, this idea is broadly applicable in many contexts.
Shielding federated learning systems against inference attacks with ARM TrustZone

Federated Learning (FL) opens new perspectives for training machine learning models while keeping personal data on the users premises. Specifically, in FL, models are trained on the users' devices and only model updates (i.e., gradients) are sent to a central server for aggregation purposes. However, the long list of inference attacks that leak private data from gradients, published in the recent years, have emphasized the need of devising effective protection mechanisms to incentivize the adoption of FL at scale. While there exist solutions to mitigate these attacks on the server side, little has been done to protect users from attacks performed on the client side. In this context, the use of Trusted Execution Environments (TEEs) on the client side are among the most proposing solutions. However, existing frameworks (e.g., DarkneTZ) require statically putting a large portion of the machine learning model into the TEE to effectively protect against complex attacks or a combination of attacks. We present GradSec, a solution that allows protecting in a TEE only sensitive layers of a machine learning model, either statically or dynamically, hence reducing both the Trusted Computing Base (TCB) size and the overall training time by up to 30% and 56%, respectively compared to state-of-the-art competitors.
Phase Matching for Multimode Four-Wave Mixing in Few-Mode Fibers and Nano-Rib Waveguides

We compare phase matching for four-wave mixing using one, two, three, and four waveguide modes. For the comparison, we use numerical optimizations and an estimate of the generated idler power. We present results for few-mode fibers and nano-rib waveguides and show that for both waveguide types, four-wave mixing bandwidths and idler powers are best for one- and two-mode operation and that four-mode four-wave mixing is not feasible at all. Some nano-rib waveguides support three-mode four-wave mixing, albeit with much reduced bandwidth and reduced idler power.
OzDES Reverberation Mapping Programme: Mg ii lags and R−L relation

The correlation between the broad line region radius and continuum luminosity (R–L relation) of active galactic nuclei (AGNs) is critical for single-epoch mass estimates of supermassive black holes (SMBHs). At z ∼ 1–2, where AGN activity peaks, the R–L relation is constrained by the reverberation mapping (RM) lags of the Mg ii line. We present 25 Mg ii lags from the Australian Dark Energy Survey RM project based on 6 yr of monitoring. We define quantitative criteria to select good lag measurements and verify their reliability with simulations based on both the damped random walk stochastic model and the rescaled, resampled versions of the observed light curves of local, well-measured AGN. Our sample significantly increases the number of Mg ii lags and extends the R–L relation to higher redshifts and luminosities. The relative iron line strength $\mathcal {R}_{\rm Fe}$ has little impact on the R–L relation. The best-fitting Mg iiR–L relation has a slope α = 0.39 ± 0.08 with an intrinsic scatter $\sigma _{\rm rl} = 0.15^{+0.03}_{-0.02}$ . The slope is consistent with previous measurements and shallower than the H β R–L relation. The intrinsic scatter of the new R–L relation is substantially smaller than previous studies and comparable to the intrinsic scatter of the H β R–L relation. Our new R–L relation will enable more precise single-epoch mass estimates and SMBH demographic studies at cosmic noon.
Chewing Detection from Commercial Smart-glasses

Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored. Automatic detection of eating based on chewing sounds has been studied extensively, however, it requires a microphone to be mounted on the subject's head for capturing the relevant sounds. In this work, we evaluate the feasibility of using an off-the-shelf commercial device, the Razer Anzu smart-glasses, for automatic chewing detection. The smart-glasses are equipped with stereo speakers and microphones that communicate with smart-phones via Bluetooth. The microphone placement is not optimal for capturing chewing sounds, however, we find that it does not significantly affect the detection effectiveness. We apply an algorithm from literature with some adjustments on a challenging dataset that we have collected in house. Leave-one-subject-out experiments yield promising results, with an F1-score of $0.96$ for the best case of duration-based evaluation of eating time.
From a Multi-modal Intelligent Cell to a Self-organizing Robotic Skin - Realizing Self and Enriching Robot Tactile Interaction

In this work, novel ways of creating and automatically organizing a modular artificial robotic skin are introduced. We present algorithms to automatically handle a scalable surface sensor network and to quickly acquire an egocentric robot body schema - inferring the robot's own kinematic and volumetric model. Exemplary applications are shown, utilizing this body knowledge to enrich the robot's multi-modal, tactile interaction capabilities.
Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality

Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous works touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique, but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. Our study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. Our study looks intointra and inter-dataset detectability to evaluate the generalizability of such a detection concept on different morphing techniques and bona fide sources. Our final results point out that a set of quality measures, such as MagFace and CNNNIQA, can be used to perform unsupervised and generalized MAD with a correct classification accuracy of over 70%.
Foundations of Monte Carlo methods and stochastic simulations - From Monte Carlo Lebesgue integration to weak approximation of SDEs

In recent years dynamical systems (of deterministic and stochastic nature), describing many models in mathematics, physics, engineering and finances, become more and more complex. Numerical analysis narrowed only to deterministic algorithms seems to be insufficient for such systems, since, for example, curse of dimensionality affects deterministic methods. Therefore, we can observe increasing popularity of Monte Carlo algorithms and, closely related with them, stochastic simulations based on stochastic differential equations. In these lecture notes we present main ideas concerned with Monte Carlo methods and their theoretical properties. We apply them to such problems as integration and approximation of solutions of deterministic/stochastic differential equations. We also discuss implementation of exemplary algorithms in Python programming language and their application to option pricing. Part of these notes has been used during lectures for PhD students at AGH University of Science and Technology, Krakow, Poland, at summer semesters in the years 2020, 2021, 2023.
A Trust-Based Malicious RSU Detection Mechanism in Edge-Enabled Vehicular Ad Hoc Networks

Edge-enabled Vehicular Ad Hoc Network (VANET) introduces real-time services and storage, computation, and communication facilities to the vehicles through Roadside Units (RSUs). Nevertheless, RSUs are often easy targets for security assaults due to their placement in an open, unprotected environment and resource-constrained nature. The malicious RSUs compromised by security attacks impose threats to human safety by impeding the operations of VANETs. Hence, an effective malevolent RSU detection mechanism is crucial for VANETs. Existing trust-based detection mechanisms assign trust scores to RSUs based on their interactions with moving vehicles where precise detection of rogue RSUs depends on the accuracy of trust scores. However, brief interaction of RSUs with the running vehicles permits inadequate time to estimate trust accurately. Besides, current works use only vehicle speed and density in beacon messages to assess trust without considering the sensor-detected data in the same messages. Nonetheless, sensor data is useful for traffic management, and neglecting them creates inaccuracy in trust estimation. In this paper, we address these limitations and propose a trust-based scheme to detect malicious RSUs that uses stable and frequent RSU-to-RSU (R2R) interaction to precisely analyze the behavior of an RSU. We also offer a mechanism to detect alteration of sensor-detected data in beacon content and incorporate this scheme in the trust calculation of RSUs. The experimental results show that the proposed solution effectively detects approximately 92% malicious RSUs, even in the presence of hostile vehicles. Moreover, integrating the proposed solution with the VANET routing protocols improves routing efficiency.
Valid Inference after Causal Discovery

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to"double dipping,"invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates; on the other hand, applying our method provides reliable coverage while achieving more accurate causal discovery than data splitting.
Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms

Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) require a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time-consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, we present a significant augmentation of the current models used in simulators for driver behavior. In this paper, we present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. In addition, we decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human-interpretable system that can be tuned to represent different classes of drivers. Additionally, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of various human-specific and system-specific factors, studying their effect on traffic network performance and safety.
Towards Automated Key-Point Detection in Images with Partial Pool View

Sports analytics has been an up-and-coming field of research among professional sporting organizations and academic institutions alike. With the insurgence and collection of athlete data, the primary goal of such analysis is to improve athletes' performance in a measurable and quantifiable manner. This work is aimed at alleviating some of the challenges encountered in the collection of adequate swimming data. Past works on this subject have shown that the detection and tracking of swimmers is feasible, but not without challenges. Among these challenges are pool localization and determining the relative positions of the swimmers relative to the pool. This work presents two contributions towards solving these challenges. First, we present a pool model with invariant key-points relevant for swimming analytics. Second, we study the detectability of such key-points in images with partial pool view, which are challenging but also quite common in swimming race videos.
Learning Computation Bounds for Branch-and-Bound Algorithms to k-plex Extraction

k-plex is a representative definition of communities in networks. While the cliques is too stiff to applicate to real cases, the k-plex relaxes the notion of the clique, allowing each node to miss up to k connections. Although k-plexes are more flexible than cliques, finding them is more challenging as their number is greater. In this paper, we aim to detect the k-plex under the size and time constraints, leveraging the new vision of automated learning bounding strategy. We introduce the constraint learning concept to learn the bound strategy from the branch and bound process and develop it into a Mixed Integer Programming framework. While most of the work is dedicated on learn the branch strategy in branch and bound-based algorithms, we focus on the learn to bound strategy which needs to handle the problem that learned strategy might not examine the feasible solution. We adopted the MILP framework and design a set of variables relative to the k-plex property as our constraint space to learn the strategy. The learn to bound strategy learning the original strategy function also reduces the computation load of the bound process to accelerate the branch and bound algorithm. Note that the learn to bound concept can apply to any branch and bound based algorithm with the appropriate framework. We conduct the experiment on different networks, the results show that our learn to branch and bound method does accelerate the original branch and bound method and outperforms other baselines, while also being able to generalize on different graph properties.
SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated

Clustering analysis is one of the critical tasks in machine learning. Traditionally, clustering has been an independent task, separate from outlier detection. Due to the fact that the performance of clustering can be significantly eroded by outliers, a small number of algorithms try to incorporate outlier detection in the process of clustering. However, most of those algorithms are based on unsupervised partition-based algorithms such as k-means. Given the nature of those algorithms, they often fail to deal with clusters of complex, non-convex shapes. To tackle this challenge, we have proposed SSDBCODI, a semi-supervised density-based algorithm. SSDBCODI combines the advantage of density-based algorithms, which are capable of dealing with clusters of complex shapes, with the semi-supervised element, which offers flexibility to adjust the clustering results based on a few user labels. We also merge an outlier detection component with the clustering process. Potential outliers are detected based on three scores generated during the process: (1) reachability-score, which measures how density-reachable a point is to a labeled normal object, (2) local-density-score, which measures the neighboring density of data objects, and (3) similarity-score, which measures the closeness of a point to its nearest labeled outliers. Then in the following step, instance weights are generated for each data instance based on those three scores before being used to train a classifier for further clustering and outlier detection. To enhance the understanding of the proposed algorithm, for our evaluation, we have run our proposed algorithm against some of the state-of-art approaches on multiple datasets and separately listed the results of outlier detection apart from clustering. Our results indicate that our algorithm can achieve superior results with a small percentage of labels.
Optical Non‐Reciprocity in Coupled Resonators by Detailed Balance

Inspired by the photosynthetic energy transfer process, a method to realize non‐reciprocal optical transmission in an array of coupled resonators is theoretically proposed. The optical non‐reciprocity of the coupled resonators arises from the frequency gradient between adjacent cavities and the interaction with the environment, which is similar to photosynthetic energy transfer. An increase in the frequency gradient or the number of the cavities can lead to better non‐reciprocity. However, although a higher environment temperature will increase the total photon number in the coupled cavities, non‐reciprocity will be weakened. All these findings can be well described by the detailed balance. The similarity between the noise‐induced optical non‐reciprocity and exciton energy transfer in natural photosynthesis is revealed by the discovery.
Upper Bounds on Mixing Time of Finite Markov Chains

We provide a general framework for computing upper bounds on mixing times of finite Markov chains when its minimal ideal is left zero. Our analysis is based on combining results by Brown and Diaconis with our previous work on stationary distributions of finite Markov chains. Stationary distributions can be computed from the Karnofsky--Rhodes and McCammond expansion of the right Cayley graph of the finite semigroup underlying the Markov chain. Using loop graphs, which are planar graphs consisting of a straight line with attached loops, there are rational expressions for the stationary distribution in the probabilities. From these we obtain bounds on the mixing time. In addition, we provide a new Markov chain on linear extension of a poset with $n$ vertices, inspired by but different from the promotion Markov chain of Ayyer, Klee and the last author. The mixing time of this Markov chain is $O(n \log n)$.
Effects of orally administered hormonal contraceptives on the musculoskeletal system of healthy premenopausal women—A systematic review

The musculoskeletal system (MSK) is one of the extragonadal target tissues of sex hormones: osteoblasts and osteocytes express estrogen receptors, while in fibroblasts of the anterior cruciate ligament (ACL) and myocytes of the vastus lateralis muscle (MVL), estrogen and progesterone receptors can be detected by immunoassay. Indeed, upon binding of sex hormones to the extragonadal receptors, the MSK seems to respond to varying levels of sex hormones with structural adaptation. Hormonal contraceptives can affect the musculoskeletal system; however, there is a lack of high‐quality studies, and no recommendation for female athletes exists.
Overcoming Limitations of Uncalibrated Robotics Visual Servoing by means of Sliding Mode Control and Switching Monitoring Scheme

This paper addresses the visual servoing control problem for robot manipulators without using velocity measurements, and considering a fixed but uncalibrated camera with an optical axis perpendicular to the robot workspace. A novel visual servoing strategy via sliding mode control (SMC) and a monitoring function based switching scheme is presented to deal with the uncertainties in the camera calibration parameters and to remove any restriction on the unknown camera orientation angle. The developed method provides global stability, disturbance rejection properties, and exact output tracking with better transient performance than adaptive controllers. Experimental results illustrate the robustness and practical feasibility of the proposed scheme.
Harnessing gauge fields for maximally entangled state generation

We study the generation of entanglement between two species of bosons living on a ring lattice, where each group of particles can be described by a d-dimensional Hilbert space (qudit). Gauge fields are exploited to create an entangled state between the pair of qudits. Maximally entangled eigenstates are found for well-defined values of the Aharonov-Bohm phase, which are zero-energy eigenstates of both the kinetic and interacting parts of the Bose-Hubbard Hamiltonian, making them quite exceptional and robust. We propose a protocol to reach the maximally entangled state (MES) by starting from an initially prepared ground state. Also, an indirect method to detect the MES by measuring the current of the particles is proposed.